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docs: turbo4 full characterization — PPL, prefill, decode, NIAH, KLD, PDF

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Complete turbo4 4-bit PolarQuant benchmark suite:
- PPL: +0.23% vs q8_0 (best after q8_0, better than q4_0)
- Prefill: 101-105% of q8_0 across 2K-32K
- Decode: 79.87 tok/s (93% q8_0, faster than turbo3)
- NIAH: 7/9 (matches q8_0)
- KLD: 0.0096 (40% lower than turbo3, matches q4_0 same-top-p)
- PDF 24K: 63.7 tok/s decode (20% faster than turbo3)

Also includes turbo4-resurrection.md paper documenting the full journey
from PPL 679 to beating q4_0 quality.

Co-Authored-By: tturney@psyguard.ai
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
TheTomClaude Opus 4.6
 committed 2026-03-28 14:19:35 -05:00
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@@ -22,3 +22,5 @@ niah_results*/
scripts/.turbo-diag-venv/
niah_decay_test/
niah_v2_results/
wikitext-103-raw/
benchmark-results-raw/*.kld
README.md
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@@ -37,37 +37,64 @@ Validated end-to-end on Qwen 3.5 35B-A3B (MoE) on M5 Max via llama.cpp Metal.
### Top-of-Tree Results
| Cache Type | Compression | Prefill tok/s | PPL (wikitext-2) | vs q8_0 prefill |
|------------|-------------|--------------|-------------------|---------------|
| f16 | 1.0x | — | 6.121 | — |
| q8_0 | 2.0x | 2694 | 5.414 | baseline |
| q4_0 | 4.0x | — | 6.142 | — |
| **turbo3** | **4.6x** | **2747** | **5.445** | **1.02x** |
| Cache Type | Bits/val | Compression | PPL (wikitext-2, 512c) | vs q8_0 |
|------------|----------|-------------|----------------------|---------|
| f16 | 16.0 | 1.0x | 6.121 | -0.16% |
| q8_0 | 8.5 | 1.9x | 6.111 | baseline |
| **turbo4** | **4.25** | **3.8x** | **6.125** | **+0.23%** |
| q4_0 | 4.5 | 3.6x | 6.142 | +0.52% |
| turbo3 | 3.5 | 4.6x | 6.176 | +1.06% |
4.6x compression. ~1% perplexity increase vs q8_0 due to compression; sparse V introduces no additional degradation.
turbo4 (4-bit PolarQuant) has the best quality after q8_0 — closer to q8_0 than q4_0, at better compression. turbo3 trades quality for maximum compression.
### Context Scaling (Verified 2K-32K)
### Prefill Context Scaling (Verified 2K-32K)
| Context | turbo3 tok/s | q8_0 tok/s | turbo3/q8_0 |
|---------|-------------|-----------|-------------|
| 2K | 4694 | 4756 | 0.987x |
| 4K | 3049 | 3084 | 0.989x |
| 8K | 2287 | 2299 | 0.995x |
| 16K | 1737 | 1757 | 0.989x |
| 32K | 1211 | 1217 | 0.995x |
| Context | turbo4 tok/s | turbo3 tok/s | q8_0 tok/s | turbo4/q8_0 | turbo3/q8_0 |
|---------|-------------|-------------|-----------|------------|------------|
| 2K | 2682 | 2708 | 2665 | 1.01x | 1.02x |
| 4K | 2370 | 2289 | 2255 | 1.05x | 1.01x |
| 8K | 2041 | 2054 | 2002 | 1.02x | 1.03x |
| 16K | 1621 | 1698 | 1605 | 1.01x | 1.06x |
| 32K | 1141 | 1204 | 1098 | 1.04x | 1.10x |
**Prefill: flat 99% of q8_0 speed regardless of context length.**
**Prefill: both turbo3 and turbo4 match or exceed q8_0 speed.** Compressed cache uses less bandwidth.
### Decode Speed — MoE (M5 Max 128GB, Qwen3.5-35B-A3B, Sparse V)
| Context | turbo3 decode | q8_0 decode | turbo3/q8_0 |
|---------|-------------|-----------|-------------|
| Short (~12 tok) | 77.6 | 86.3 | 0.90x |
| 4K | 74.9 | — | — |
| 8K | 71.7 | — | — |
| 16K | 66.5 | 72.0 | 0.92x |
| 24K (70-page PDF) | 53.3 | 68.2 | 0.78x |
| 32K | 57.7 | 62.0 | **0.93x** |
| Config | Short decode (tg128) | pp32768+tg128 | Short vs q8_0 |
|--------|---------------------|---------------|--------------|
| q8_0 | 85.71 tok/s | 1173.91 tok/s | — |
| **turbo4** | **79.87 tok/s** | **1060.12 tok/s** | **0.93x** |
| turbo3 | 76.84 tok/s | 1141.74 tok/s | 0.90x |
turbo4 decode is faster than turbo3 due to simpler nibble packing and direct-extract dequant.
**Real-world server benchmark (70-page PDF, ~24K context):**
| Config | Prefill tok/s | Decode tok/s | Decode vs q8_0 |
|--------|-------------|-------------|---------------|
| q8_0 | 1449.9 | 68.2 | — |
| turbo4 | 1405.9 | 63.7 | 0.93x |
| turbo3 | 1417.8 | 53.3 | 0.78x |
### NIAH Retrieval (turbo4)
| Test | q8_0 | turbo4 | turbo3 + sparse V |
|------|------|--------|-------------------|
| Single needle (9 positions) | 7/9 | 7/9 | 9/9 |
turbo4 matches q8_0 retrieval accuracy.
### KL Divergence vs f16
| Cache | Mean KLD | Δp RMS | Same top-p % |
|-------|----------|--------|-------------|
| q8_0 | 0.001549 | 1.23% | 98.43% |
| **turbo4** | **0.009633** | **2.71%** | **95.98%** |
| q4_0 | 0.008091 | 2.75% | 95.83% |
| turbo3 | 0.016145 | 4.09% | 94.31% |
turbo4 KLD is 40% lower than turbo3. Same top-p agreement matches q4_0.
### Decode Speed — Dense (M5 Max 128GB, Qwen3.5-27B, Sparse V)
benchmark-results-raw/kld_dense_q4_0.log
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ggml_metal_device_init: testing tensor API for f16 support
ggml_metal_library_compile_pipeline: compiling pipeline: base = 'dummy_kernel', name = 'dummy_kernel'
ggml_metal_library_compile_pipeline: loaded dummy_kernel 0x104561880 | th_max = 1024 | th_width = 32
ggml_metal_device_init: testing tensor API for bfloat support
ggml_metal_library_compile_pipeline: compiling pipeline: base = 'dummy_kernel', name = 'dummy_kernel'
ggml_metal_library_compile_pipeline: loaded dummy_kernel 0x104561500 | th_max = 1024 | th_width = 32
ggml_metal_library_init: using embedded metal library
ggml_metal_library_init: turbo3 sparse V dequant enabled
ggml_metal_library_init: loaded in 0.008 sec
ggml_metal_rsets_init: creating a residency set collection (keep_alive = 180 s)
ggml_metal_device_init: GPU name: MTL0
ggml_metal_device_init: GPU family: MTLGPUFamilyApple10 (1010)
ggml_metal_device_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_device_init: GPU family: MTLGPUFamilyMetal4 (5002)
ggml_metal_device_init: simdgroup reduction = true
ggml_metal_device_init: simdgroup matrix mul. = true
ggml_metal_device_init: has unified memory = true
ggml_metal_device_init: has bfloat = true
ggml_metal_device_init: has tensor = true
ggml_metal_device_init: use residency sets = true
ggml_metal_device_init: use shared buffers = true
ggml_metal_device_init: recommendedMaxWorkingSetSize = 115448.73 MB
build: 159 (7d1bd95) with AppleClang 17.0.0.17000604 for Darwin arm64
common_init_result: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on
llama_params_fit_impl: projected to use 27101 MiB of device memory vs. 109501 MiB of free device memory
llama_params_fit_impl: will leave 82399 >= 1024 MiB of free device memory, no changes needed
llama_params_fit: successfully fit params to free device memory
llama_params_fit: fitting params to free memory took 0.44 seconds
llama_model_load_from_file_impl: using device MTL0 (Apple M5 Max) (unknown id) - 110100 MiB free
llama_model_loader: loaded meta data with 49 key-value pairs and 851 tensors from /Users/tom/local_llms/models/Qwen3.5-27B-Q8_0.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = qwen35
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.sampling.top_k i32 = 20
llama_model_loader: - kv 3: general.sampling.top_p f32 = 0.950000
llama_model_loader: - kv 4: general.sampling.temp f32 = 0.600000
llama_model_loader: - kv 5: general.name str = Qwen3.5-27B
llama_model_loader: - kv 6: general.basename str = Qwen3.5-27B
llama_model_loader: - kv 7: general.quantized_by str = Unsloth
llama_model_loader: - kv 8: general.size_label str = 27B
llama_model_loader: - kv 9: general.license str = apache-2.0
llama_model_loader: - kv 10: general.license.link str = https://huggingface.co/Qwen/Qwen3.5-2...
llama_model_loader: - kv 11: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 12: general.base_model.count u32 = 1
llama_model_loader: - kv 13: general.base_model.0.name str = Qwen3.5 27B
llama_model_loader: - kv 14: general.base_model.0.organization str = Qwen
llama_model_loader: - kv 15: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen3.5-27B
llama_model_loader: - kv 16: general.tags arr[str,3] = ["qwen3_5_moe", "unsloth", "image-tex...
llama_model_loader: - kv 17: qwen35.block_count u32 = 64
llama_model_loader: - kv 18: qwen35.context_length u32 = 262144
llama_model_loader: - kv 19: qwen35.embedding_length u32 = 5120
llama_model_loader: - kv 20: qwen35.feed_forward_length u32 = 17408
llama_model_loader: - kv 21: qwen35.attention.head_count u32 = 24
llama_model_loader: - kv 22: qwen35.attention.head_count_kv u32 = 4
llama_model_loader: - kv 23: qwen35.rope.dimension_sections arr[i32,4] = [11, 11, 10, 0]
llama_model_loader: - kv 24: qwen35.rope.freq_base f32 = 10000000.000000
llama_model_loader: - kv 25: qwen35.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 26: qwen35.attention.key_length u32 = 256
llama_model_loader: - kv 27: qwen35.attention.value_length u32 = 256
llama_model_loader: - kv 28: qwen35.ssm.conv_kernel u32 = 4
llama_model_loader: - kv 29: qwen35.ssm.state_size u32 = 128
llama_model_loader: - kv 30: qwen35.ssm.group_count u32 = 16
llama_model_loader: - kv 31: qwen35.ssm.time_step_rank u32 = 48
llama_model_loader: - kv 32: qwen35.ssm.inner_size u32 = 6144
llama_model_loader: - kv 33: qwen35.full_attention_interval u32 = 4
llama_model_loader: - kv 34: qwen35.rope.dimension_count u32 = 64
llama_model_loader: - kv 35: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 36: tokenizer.ggml.pre str = qwen35
llama_model_loader: - kv 37: tokenizer.ggml.tokens arr[str,248320] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 38: tokenizer.ggml.token_type arr[i32,248320] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 39: tokenizer.ggml.merges arr[str,247587] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 40: tokenizer.ggml.eos_token_id u32 = 248046
llama_model_loader: - kv 41: tokenizer.ggml.padding_token_id u32 = 248055
llama_model_loader: - kv 42: tokenizer.chat_template str = {%- set image_count = namespace(value...
llama_model_loader: - kv 43: general.quantization_version u32 = 2
llama_model_loader: - kv 44: general.file_type u32 = 7
llama_model_loader: - kv 45: quantize.imatrix.file str = Qwen3.5-27B-GGUF/imatrix_unsloth.gguf
llama_model_loader: - kv 46: quantize.imatrix.dataset str = unsloth_calibration_Qwen3.5-27B.txt
llama_model_loader: - kv 47: quantize.imatrix.entries_count u32 = 496
llama_model_loader: - kv 48: quantize.imatrix.chunks_count u32 = 80
llama_model_loader: - type f32: 353 tensors
llama_model_loader: - type q8_0: 498 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q8_0
print_info: file size = 26.62 GiB (8.50 BPW)
load: 0 unused tokens
load: printing all EOG tokens:
load: - 248044 ('<|endoftext|>')
load: - 248046 ('<|im_end|>')
load: - 248063 ('<|fim_pad|>')
load: - 248064 ('<|repo_name|>')
load: - 248065 ('<|file_sep|>')
load: special tokens cache size = 33
load: token to piece cache size = 1.7581 MB
print_info: arch = qwen35
print_info: vocab_only = 0
print_info: no_alloc = 0
print_info: n_ctx_train = 262144
print_info: n_embd = 5120
print_info: n_embd_inp = 5120
print_info: n_layer = 64
print_info: n_head = 24
print_info: n_head_kv = 4
print_info: n_rot = 64
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 256
print_info: n_embd_head_v = 256
print_info: n_gqa = 6
print_info: n_embd_k_gqa = 1024
print_info: n_embd_v_gqa = 1024
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: n_ff = 17408
print_info: n_expert = 0
print_info: n_expert_used = 0
print_info: n_expert_groups = 0
print_info: n_group_used = 0
print_info: causal attn = 1
print_info: pooling type = -1
print_info: rope type = 40
print_info: rope scaling = linear
print_info: freq_base_train = 10000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 262144
print_info: rope_yarn_log_mul = 0.0000
print_info: rope_finetuned = unknown
print_info: mrope sections = [11, 11, 10, 0]
print_info: ssm_d_conv = 4
print_info: ssm_d_inner = 6144
print_info: ssm_d_state = 128
print_info: ssm_dt_rank = 48
print_info: ssm_n_group = 16
print_info: ssm_dt_b_c_rms = 0
print_info: model type = 27B
print_info: model params = 26.90 B
print_info: general.name = Qwen3.5-27B
print_info: vocab type = BPE
print_info: n_vocab = 248320
print_info: n_merges = 247587
print_info: BOS token = 11 ','
print_info: EOS token = 248046 '<|im_end|>'
print_info: EOT token = 248046 '<|im_end|>'
print_info: PAD token = 248055 '<|vision_pad|>'
print_info: LF token = 198 'Ċ'
print_info: FIM PRE token = 248060 '<|fim_prefix|>'
print_info: FIM SUF token = 248062 '<|fim_suffix|>'
print_info: FIM MID token = 248061 '<|fim_middle|>'
print_info: FIM PAD token = 248063 '<|fim_pad|>'
print_info: FIM REP token = 248064 '<|repo_name|>'
print_info: FIM SEP token = 248065 '<|file_sep|>'
print_info: EOG token = 248044 '<|endoftext|>'
print_info: EOG token = 248046 '<|im_end|>'
print_info: EOG token = 248063 '<|fim_pad|>'
print_info: EOG token = 248064 '<|repo_name|>'
print_info: EOG token = 248065 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = true, direct_io = false)
load_tensors: offloading output layer to GPU
load_tensors: offloading 63 repeating layers to GPU
load_tensors: offloaded 65/65 layers to GPU
load_tensors: CPU_Mapped model buffer size = 1288.28 MiB
load_tensors: MTL0_Mapped model buffer size = 27260.56 MiB
.............................................................................................
common_init_result: added <|endoftext|> logit bias = -inf
common_init_result: added <|im_end|> logit bias = -inf
common_init_result: added <|fim_pad|> logit bias = -inf
common_init_result: added <|repo_name|> logit bias = -inf
common_init_result: added <|file_sep|> logit bias = -inf
llama_context: constructing llama_context
llama_context: n_seq_max = 4
llama_context: n_ctx = 2048
llama_context: n_ctx_seq = 512
llama_context: n_batch = 2048
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = enabled
llama_context: kv_unified = false
llama_context: freq_base = 10000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_seq (512) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M5 Max
ggml_metal_init: picking default device: Apple M5 Max
ggml_metal_init: use fusion = true
ggml_metal_init: use concurrency = true
ggml_metal_init: use graph optimize = true
llama_context: CPU output buffer size = 3.79 MiB
llama_kv_cache: MTL0 KV buffer size = 36.00 MiB
llama_kv_cache: size = 36.00 MiB ( 512 cells, 16 layers, 4/4 seqs), K (q4_0): 18.00 MiB, V (q4_0): 18.00 MiB
llama_memory_recurrent: MTL0 RS buffer size = 598.50 MiB
llama_memory_recurrent: size = 598.50 MiB ( 4 cells, 64 layers, 4 seqs), R (f32): 22.50 MiB, S (f32): 576.00 MiB
sched_reserve: reserving ...
sched_reserve: resolving fused Gated Delta Net support:
sched_reserve: fused Gated Delta Net (autoregressive) enabled
sched_reserve: fused Gated Delta Net (chunked) enabled
sched_reserve: MTL0 compute buffer size = 495.00 MiB
sched_reserve: CPU compute buffer size = 21.02 MiB
sched_reserve: graph nodes = 3689
sched_reserve: graph splits = 2
sched_reserve: reserve took 21.91 ms, sched copies = 1
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
system_info: n_threads = 6 (n_threads_batch = 6) / 18 | MTL : EMBED_LIBRARY = 1 | CPU : NEON = 1 | ARM_FMA = 1 | FP16_VA = 1 | MATMUL_INT8 = 1 | DOTPROD = 1 | SME = 1 | ACCELERATE = 1 | REPACK = 1 |
kl_divergence: computing over 8 chunks, n_ctx=512, batch_size=2048, n_seq=4
kl_divergence: 4.24 seconds per pass - ETA 0.13 minutes
chunk PPL ln(PPL(Q)/PPL(base)) KL Divergence Δp RMS Same top p
1 4.3701 ± 0.6250 -0.00411 ± 0.00599 0.00272 ± 0.00033 1.726 ± 0.239 % 96.863 ± 1.094 %
2 6.1576 ± 0.7183 -0.00785 ± 0.00383 0.00311 ± 0.00027 1.566 ± 0.144 % 97.255 ± 0.724 %
3 6.1263 ± 0.5853 -0.00665 ± 0.00545 0.00303 ± 0.00021 1.487 ± 0.111 % 97.124 ± 0.605 %
4 6.1484 ± 0.4897 -0.00398 ± 0.00431 0.00319 ± 0.00027 1.646 ± 0.224 % 97.353 ± 0.503 %
5 6.0643 ± 0.4175 -0.00294 ± 0.00357 0.00298 ± 0.00022 1.580 ± 0.188 % 97.490 ± 0.438 %
6 6.2810 ± 0.3940 -0.00177 ± 0.00304 0.00287 ± 0.00018 1.527 ± 0.164 % 97.778 ± 0.377 %
7 6.4068 ± 0.3694 -0.00028 ± 0.00281 0.00281 ± 0.00016 1.485 ± 0.145 % 97.647 ± 0.359 %
8 6.8788 ± 0.3816 0.00086 ± 0.00254 0.00274 ± 0.00014 1.437 ± 0.131 % 97.647 ± 0.336 %
====== Perplexity statistics ======
Mean PPL(Q) : 6.878806 ± 0.381592
Mean PPL(base) : 6.872893 ± 0.379370
Cor(ln(PPL(Q)), ln(PPL(base))): 99.90%
Mean ln(PPL(Q)/PPL(base)) : 0.000860 ± 0.002542
Mean PPL(Q)/PPL(base) : 1.000860 ± 0.002544
Mean PPL(Q)-PPL(base) : 0.005913 ± 0.017515
====== KL divergence statistics ======
Mean KLD: 0.002741 ± 0.000142
Maximum KLD: 0.210398
99.9% KLD: 0.073911
99.0% KLD: 0.020144
95.0% KLD: 0.008533
90.0% KLD: 0.005757
Median KLD: 0.001445
10.0% KLD: 0.000109
5.0% KLD: 0.000029
1.0% KLD: 0.000003
0.1% KLD: -0.000001
Minimum KLD: -0.000017
====== Token probability statistics ======
Mean Δp: 0.004 ± 0.032 %
Maximum Δp: 26.606%
99.9% Δp: 8.512%
99.0% Δp: 4.052%
95.0% Δp: 1.952%
90.0% Δp: 1.174%
75.0% Δp: 0.292%
Median Δp: -0.000%
25.0% Δp: -0.355%
10.0% Δp: -1.232%
5.0% Δp: -1.915%
1.0% Δp: -3.607%
0.1% Δp: -8.014%
Minimum Δp: -11.468%
RMS Δp : 1.437 ± 0.131 %
Same top p: 97.647 ± 0.336 %
llama_perf_context_print: load time = 1930.89 ms
llama_perf_context_print: prompt eval time = 4412.57 ms / 4096 tokens ( 1.08 ms per token, 928.26 tokens per second)
llama_perf_context_print: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
llama_perf_context_print: total time = 8975.85 ms / 4097 tokens
llama_perf_context_print: graphs reused = 2
llama_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted |
llama_memory_breakdown_print: | - MTL0 (Apple M5 Max) | 110100 = 81709 + (28390 = 27260 + 634 + 495) + 0 |
llama_memory_breakdown_print: | - Host | 1309 = 1288 + 0 + 21 |
ggml_metal_free: deallocating
benchmark-results-raw/kld_dense_q8_0.log
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ggml_metal_device_init: testing tensor API for f16 support
ggml_metal_library_compile_pipeline: compiling pipeline: base = 'dummy_kernel', name = 'dummy_kernel'
ggml_metal_library_compile_pipeline: loaded dummy_kernel 0x10261aea0 | th_max = 1024 | th_width = 32
ggml_metal_device_init: testing tensor API for bfloat support
ggml_metal_library_compile_pipeline: compiling pipeline: base = 'dummy_kernel', name = 'dummy_kernel'
ggml_metal_library_compile_pipeline: loaded dummy_kernel 0x10261ab20 | th_max = 1024 | th_width = 32
ggml_metal_library_init: using embedded metal library
ggml_metal_library_init: turbo3 sparse V dequant enabled
ggml_metal_library_init: loaded in 0.009 sec
ggml_metal_rsets_init: creating a residency set collection (keep_alive = 180 s)
ggml_metal_device_init: GPU name: MTL0
ggml_metal_device_init: GPU family: MTLGPUFamilyApple10 (1010)
ggml_metal_device_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_device_init: GPU family: MTLGPUFamilyMetal4 (5002)
ggml_metal_device_init: simdgroup reduction = true
ggml_metal_device_init: simdgroup matrix mul. = true
ggml_metal_device_init: has unified memory = true
ggml_metal_device_init: has bfloat = true
ggml_metal_device_init: has tensor = true
ggml_metal_device_init: use residency sets = true
ggml_metal_device_init: use shared buffers = true
ggml_metal_device_init: recommendedMaxWorkingSetSize = 115448.73 MB
build: 159 (7d1bd95) with AppleClang 17.0.0.17000604 for Darwin arm64
common_init_result: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on
llama_params_fit_impl: projected to use 27133 MiB of device memory vs. 109501 MiB of free device memory
llama_params_fit_impl: will leave 82367 >= 1024 MiB of free device memory, no changes needed
llama_params_fit: successfully fit params to free device memory
llama_params_fit: fitting params to free memory took 0.30 seconds
llama_model_load_from_file_impl: using device MTL0 (Apple M5 Max) (unknown id) - 110100 MiB free
llama_model_loader: loaded meta data with 49 key-value pairs and 851 tensors from /Users/tom/local_llms/models/Qwen3.5-27B-Q8_0.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = qwen35
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.sampling.top_k i32 = 20
llama_model_loader: - kv 3: general.sampling.top_p f32 = 0.950000
llama_model_loader: - kv 4: general.sampling.temp f32 = 0.600000
llama_model_loader: - kv 5: general.name str = Qwen3.5-27B
llama_model_loader: - kv 6: general.basename str = Qwen3.5-27B
llama_model_loader: - kv 7: general.quantized_by str = Unsloth
llama_model_loader: - kv 8: general.size_label str = 27B
llama_model_loader: - kv 9: general.license str = apache-2.0
llama_model_loader: - kv 10: general.license.link str = https://huggingface.co/Qwen/Qwen3.5-2...
llama_model_loader: - kv 11: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 12: general.base_model.count u32 = 1
llama_model_loader: - kv 13: general.base_model.0.name str = Qwen3.5 27B
llama_model_loader: - kv 14: general.base_model.0.organization str = Qwen
llama_model_loader: - kv 15: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen3.5-27B
llama_model_loader: - kv 16: general.tags arr[str,3] = ["qwen3_5_moe", "unsloth", "image-tex...
llama_model_loader: - kv 17: qwen35.block_count u32 = 64
llama_model_loader: - kv 18: qwen35.context_length u32 = 262144
llama_model_loader: - kv 19: qwen35.embedding_length u32 = 5120
llama_model_loader: - kv 20: qwen35.feed_forward_length u32 = 17408
llama_model_loader: - kv 21: qwen35.attention.head_count u32 = 24
llama_model_loader: - kv 22: qwen35.attention.head_count_kv u32 = 4
llama_model_loader: - kv 23: qwen35.rope.dimension_sections arr[i32,4] = [11, 11, 10, 0]
llama_model_loader: - kv 24: qwen35.rope.freq_base f32 = 10000000.000000
llama_model_loader: - kv 25: qwen35.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 26: qwen35.attention.key_length u32 = 256
llama_model_loader: - kv 27: qwen35.attention.value_length u32 = 256
llama_model_loader: - kv 28: qwen35.ssm.conv_kernel u32 = 4
llama_model_loader: - kv 29: qwen35.ssm.state_size u32 = 128
llama_model_loader: - kv 30: qwen35.ssm.group_count u32 = 16
llama_model_loader: - kv 31: qwen35.ssm.time_step_rank u32 = 48
llama_model_loader: - kv 32: qwen35.ssm.inner_size u32 = 6144
llama_model_loader: - kv 33: qwen35.full_attention_interval u32 = 4
llama_model_loader: - kv 34: qwen35.rope.dimension_count u32 = 64
llama_model_loader: - kv 35: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 36: tokenizer.ggml.pre str = qwen35
llama_model_loader: - kv 37: tokenizer.ggml.tokens arr[str,248320] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 38: tokenizer.ggml.token_type arr[i32,248320] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 39: tokenizer.ggml.merges arr[str,247587] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 40: tokenizer.ggml.eos_token_id u32 = 248046
llama_model_loader: - kv 41: tokenizer.ggml.padding_token_id u32 = 248055
llama_model_loader: - kv 42: tokenizer.chat_template str = {%- set image_count = namespace(value...
llama_model_loader: - kv 43: general.quantization_version u32 = 2
llama_model_loader: - kv 44: general.file_type u32 = 7
llama_model_loader: - kv 45: quantize.imatrix.file str = Qwen3.5-27B-GGUF/imatrix_unsloth.gguf
llama_model_loader: - kv 46: quantize.imatrix.dataset str = unsloth_calibration_Qwen3.5-27B.txt
llama_model_loader: - kv 47: quantize.imatrix.entries_count u32 = 496
llama_model_loader: - kv 48: quantize.imatrix.chunks_count u32 = 80
llama_model_loader: - type f32: 353 tensors
llama_model_loader: - type q8_0: 498 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q8_0
print_info: file size = 26.62 GiB (8.50 BPW)
load: 0 unused tokens
load: printing all EOG tokens:
load: - 248044 ('<|endoftext|>')
load: - 248046 ('<|im_end|>')
load: - 248063 ('<|fim_pad|>')
load: - 248064 ('<|repo_name|>')
load: - 248065 ('<|file_sep|>')
load: special tokens cache size = 33
load: token to piece cache size = 1.7581 MB
print_info: arch = qwen35
print_info: vocab_only = 0
print_info: no_alloc = 0
print_info: n_ctx_train = 262144
print_info: n_embd = 5120
print_info: n_embd_inp = 5120
print_info: n_layer = 64
print_info: n_head = 24
print_info: n_head_kv = 4
print_info: n_rot = 64
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 256
print_info: n_embd_head_v = 256
print_info: n_gqa = 6
print_info: n_embd_k_gqa = 1024
print_info: n_embd_v_gqa = 1024
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: n_ff = 17408
print_info: n_expert = 0
print_info: n_expert_used = 0
print_info: n_expert_groups = 0
print_info: n_group_used = 0
print_info: causal attn = 1
print_info: pooling type = -1
print_info: rope type = 40
print_info: rope scaling = linear
print_info: freq_base_train = 10000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 262144
print_info: rope_yarn_log_mul = 0.0000
print_info: rope_finetuned = unknown
print_info: mrope sections = [11, 11, 10, 0]
print_info: ssm_d_conv = 4
print_info: ssm_d_inner = 6144
print_info: ssm_d_state = 128
print_info: ssm_dt_rank = 48
print_info: ssm_n_group = 16
print_info: ssm_dt_b_c_rms = 0
print_info: model type = 27B
print_info: model params = 26.90 B
print_info: general.name = Qwen3.5-27B
print_info: vocab type = BPE
print_info: n_vocab = 248320
print_info: n_merges = 247587
print_info: BOS token = 11 ','
print_info: EOS token = 248046 '<|im_end|>'
print_info: EOT token = 248046 '<|im_end|>'
print_info: PAD token = 248055 '<|vision_pad|>'
print_info: LF token = 198 'Ċ'
print_info: FIM PRE token = 248060 '<|fim_prefix|>'
print_info: FIM SUF token = 248062 '<|fim_suffix|>'
print_info: FIM MID token = 248061 '<|fim_middle|>'
print_info: FIM PAD token = 248063 '<|fim_pad|>'
print_info: FIM REP token = 248064 '<|repo_name|>'
print_info: FIM SEP token = 248065 '<|file_sep|>'
print_info: EOG token = 248044 '<|endoftext|>'
print_info: EOG token = 248046 '<|im_end|>'
print_info: EOG token = 248063 '<|fim_pad|>'
print_info: EOG token = 248064 '<|repo_name|>'
print_info: EOG token = 248065 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = true, direct_io = false)
load_tensors: offloading output layer to GPU
load_tensors: offloading 63 repeating layers to GPU
load_tensors: offloaded 65/65 layers to GPU
load_tensors: CPU_Mapped model buffer size = 1288.28 MiB
load_tensors: MTL0_Mapped model buffer size = 27260.56 MiB
.............................................................................................
common_init_result: added <|endoftext|> logit bias = -inf
common_init_result: added <|im_end|> logit bias = -inf
common_init_result: added <|fim_pad|> logit bias = -inf
common_init_result: added <|repo_name|> logit bias = -inf
common_init_result: added <|file_sep|> logit bias = -inf
llama_context: constructing llama_context
llama_context: n_seq_max = 4
llama_context: n_ctx = 2048
llama_context: n_ctx_seq = 512
llama_context: n_batch = 2048
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = enabled
llama_context: kv_unified = false
llama_context: freq_base = 10000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_seq (512) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M5 Max
ggml_metal_init: picking default device: Apple M5 Max
ggml_metal_init: use fusion = true
ggml_metal_init: use concurrency = true
ggml_metal_init: use graph optimize = true
llama_context: CPU output buffer size = 3.79 MiB
llama_kv_cache: MTL0 KV buffer size = 68.00 MiB
llama_kv_cache: size = 68.00 MiB ( 512 cells, 16 layers, 4/4 seqs), K (q8_0): 34.00 MiB, V (q8_0): 34.00 MiB
llama_memory_recurrent: MTL0 RS buffer size = 598.50 MiB
llama_memory_recurrent: size = 598.50 MiB ( 4 cells, 64 layers, 4 seqs), R (f32): 22.50 MiB, S (f32): 576.00 MiB
sched_reserve: reserving ...
sched_reserve: resolving fused Gated Delta Net support:
sched_reserve: fused Gated Delta Net (autoregressive) enabled
sched_reserve: fused Gated Delta Net (chunked) enabled
sched_reserve: MTL0 compute buffer size = 495.00 MiB
sched_reserve: CPU compute buffer size = 21.02 MiB
sched_reserve: graph nodes = 3689
sched_reserve: graph splits = 2
sched_reserve: reserve took 24.55 ms, sched copies = 1
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
system_info: n_threads = 6 (n_threads_batch = 6) / 18 | MTL : EMBED_LIBRARY = 1 | CPU : NEON = 1 | ARM_FMA = 1 | FP16_VA = 1 | MATMUL_INT8 = 1 | DOTPROD = 1 | SME = 1 | ACCELERATE = 1 | REPACK = 1 |
kl_divergence: computing over 8 chunks, n_ctx=512, batch_size=2048, n_seq=4
kl_divergence: 4.36 seconds per pass - ETA 0.13 minutes
chunk PPL ln(PPL(Q)/PPL(base)) KL Divergence Δp RMS Same top p
1 4.3889 ± 0.6289 0.00019 ± 0.00033 0.00001 ± 0.00000 0.070 ± 0.006 % 100.000 ± 0.000 %
2 6.2073 ± 0.7277 0.00020 ± 0.00025 0.00001 ± 0.00000 0.089 ± 0.009 % 100.000 ± 0.000 %
3 6.1931 ± 0.5947 0.00420 ± 0.00383 0.00001 ± 0.00000 0.094 ± 0.007 % 99.869 ± 0.131 %
4 6.1925 ± 0.4950 0.00317 ± 0.00288 0.00002 ± 0.00001 0.161 ± 0.038 % 99.902 ± 0.098 %
5 6.0978 ± 0.4209 0.00257 ± 0.00230 0.00002 ± 0.00001 0.149 ± 0.033 % 99.922 ± 0.078 %
6 6.3059 ± 0.3964 0.00220 ± 0.00192 0.00002 ± 0.00000 0.139 ± 0.029 % 99.935 ± 0.065 %
7 6.4256 ± 0.3712 0.00264 ± 0.00185 0.00002 ± 0.00000 0.133 ± 0.026 % 99.888 ± 0.079 %
8 6.8884 ± 0.3822 0.00225 ± 0.00162 0.00002 ± 0.00000 0.127 ± 0.024 % 99.902 ± 0.069 %
====== Perplexity statistics ======
Mean PPL(Q) : 6.888352 ± 0.382217
Mean PPL(base) : 6.872893 ± 0.379370
Cor(ln(PPL(Q)), ln(PPL(base))): 99.96%
Mean ln(PPL(Q)/PPL(base)) : 0.002247 ± 0.001616
Mean PPL(Q)/PPL(base) : 1.002249 ± 0.001620
Mean PPL(Q)-PPL(base) : 0.015459 ± 0.011303
====== KL divergence statistics ======
Mean KLD: 0.000018 ± 0.000004
Maximum KLD: 0.006564
99.9% KLD: 0.000212
99.0% KLD: 0.000101
95.0% KLD: 0.000051
90.0% KLD: 0.000039
Median KLD: 0.000007
10.0% KLD: -0.000006
5.0% KLD: -0.000013
1.0% KLD: -0.000027
0.1% KLD: -0.000038
Minimum KLD: -0.000040
====== Token probability statistics ======
Mean Δp: -0.004 ± 0.003 %
Maximum Δp: 0.841%
99.9% Δp: 0.597%
99.0% Δp: 0.242%
95.0% Δp: 0.123%
90.0% Δp: 0.079%
75.0% Δp: 0.020%
Median Δp: 0.000%
25.0% Δp: -0.020%
10.0% Δp: -0.078%
5.0% Δp: -0.138%
1.0% Δp: -0.320%
0.1% Δp: -0.611%
Minimum Δp: -3.256%
RMS Δp : 0.127 ± 0.024 %
Same top p: 99.902 ± 0.069 %
llama_perf_context_print: load time = 1908.04 ms
llama_perf_context_print: prompt eval time = 4509.35 ms / 4096 tokens ( 1.10 ms per token, 908.34 tokens per second)
llama_perf_context_print: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
llama_perf_context_print: total time = 9208.81 ms / 4097 tokens
llama_perf_context_print: graphs reused = 2
llama_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted |
llama_memory_breakdown_print: | - MTL0 (Apple M5 Max) | 110100 = 81677 + (28422 = 27260 + 666 + 495) + 0 |
llama_memory_breakdown_print: | - Host | 1309 = 1288 + 0 + 21 |
ggml_metal_free: deallocating
benchmark-results-raw/kld_dense_turbo3.log
+271
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@@ -0,0 +1,271 @@
ggml_metal_device_init: testing tensor API for f16 support
ggml_metal_library_compile_pipeline: compiling pipeline: base = 'dummy_kernel', name = 'dummy_kernel'
ggml_metal_library_compile_pipeline: loaded dummy_kernel 0x10527e1f0 | th_max = 1024 | th_width = 32
ggml_metal_device_init: testing tensor API for bfloat support
ggml_metal_library_compile_pipeline: compiling pipeline: base = 'dummy_kernel', name = 'dummy_kernel'
ggml_metal_library_compile_pipeline: loaded dummy_kernel 0x10527cc70 | th_max = 1024 | th_width = 32
ggml_metal_library_init: using embedded metal library
ggml_metal_library_init: turbo3 sparse V dequant enabled
ggml_metal_library_init: loaded in 0.008 sec
ggml_metal_rsets_init: creating a residency set collection (keep_alive = 180 s)
ggml_metal_device_init: GPU name: MTL0
ggml_metal_device_init: GPU family: MTLGPUFamilyApple10 (1010)
ggml_metal_device_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_device_init: GPU family: MTLGPUFamilyMetal4 (5002)
ggml_metal_device_init: simdgroup reduction = true
ggml_metal_device_init: simdgroup matrix mul. = true
ggml_metal_device_init: has unified memory = true
ggml_metal_device_init: has bfloat = true
ggml_metal_device_init: has tensor = true
ggml_metal_device_init: use residency sets = true
ggml_metal_device_init: use shared buffers = true
ggml_metal_device_init: recommendedMaxWorkingSetSize = 115448.73 MB
build: 159 (7d1bd95) with AppleClang 17.0.0.17000604 for Darwin arm64
common_init_result: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on
llama_params_fit_impl: projected to use 27093 MiB of device memory vs. 109501 MiB of free device memory
llama_params_fit_impl: will leave 82407 >= 1024 MiB of free device memory, no changes needed
llama_params_fit: successfully fit params to free device memory
llama_params_fit: fitting params to free memory took 0.36 seconds
llama_model_load_from_file_impl: using device MTL0 (Apple M5 Max) (unknown id) - 110100 MiB free
llama_model_loader: loaded meta data with 49 key-value pairs and 851 tensors from /Users/tom/local_llms/models/Qwen3.5-27B-Q8_0.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = qwen35
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.sampling.top_k i32 = 20
llama_model_loader: - kv 3: general.sampling.top_p f32 = 0.950000
llama_model_loader: - kv 4: general.sampling.temp f32 = 0.600000
llama_model_loader: - kv 5: general.name str = Qwen3.5-27B
llama_model_loader: - kv 6: general.basename str = Qwen3.5-27B
llama_model_loader: - kv 7: general.quantized_by str = Unsloth
llama_model_loader: - kv 8: general.size_label str = 27B
llama_model_loader: - kv 9: general.license str = apache-2.0
llama_model_loader: - kv 10: general.license.link str = https://huggingface.co/Qwen/Qwen3.5-2...
llama_model_loader: - kv 11: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 12: general.base_model.count u32 = 1
llama_model_loader: - kv 13: general.base_model.0.name str = Qwen3.5 27B
llama_model_loader: - kv 14: general.base_model.0.organization str = Qwen
llama_model_loader: - kv 15: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen3.5-27B
llama_model_loader: - kv 16: general.tags arr[str,3] = ["qwen3_5_moe", "unsloth", "image-tex...
llama_model_loader: - kv 17: qwen35.block_count u32 = 64
llama_model_loader: - kv 18: qwen35.context_length u32 = 262144
llama_model_loader: - kv 19: qwen35.embedding_length u32 = 5120
llama_model_loader: - kv 20: qwen35.feed_forward_length u32 = 17408
llama_model_loader: - kv 21: qwen35.attention.head_count u32 = 24
llama_model_loader: - kv 22: qwen35.attention.head_count_kv u32 = 4
llama_model_loader: - kv 23: qwen35.rope.dimension_sections arr[i32,4] = [11, 11, 10, 0]
llama_model_loader: - kv 24: qwen35.rope.freq_base f32 = 10000000.000000
llama_model_loader: - kv 25: qwen35.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 26: qwen35.attention.key_length u32 = 256
llama_model_loader: - kv 27: qwen35.attention.value_length u32 = 256
llama_model_loader: - kv 28: qwen35.ssm.conv_kernel u32 = 4
llama_model_loader: - kv 29: qwen35.ssm.state_size u32 = 128
llama_model_loader: - kv 30: qwen35.ssm.group_count u32 = 16
llama_model_loader: - kv 31: qwen35.ssm.time_step_rank u32 = 48
llama_model_loader: - kv 32: qwen35.ssm.inner_size u32 = 6144
llama_model_loader: - kv 33: qwen35.full_attention_interval u32 = 4
llama_model_loader: - kv 34: qwen35.rope.dimension_count u32 = 64
llama_model_loader: - kv 35: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 36: tokenizer.ggml.pre str = qwen35
llama_model_loader: - kv 37: tokenizer.ggml.tokens arr[str,248320] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 38: tokenizer.ggml.token_type arr[i32,248320] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 39: tokenizer.ggml.merges arr[str,247587] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 40: tokenizer.ggml.eos_token_id u32 = 248046
llama_model_loader: - kv 41: tokenizer.ggml.padding_token_id u32 = 248055
llama_model_loader: - kv 42: tokenizer.chat_template str = {%- set image_count = namespace(value...
llama_model_loader: - kv 43: general.quantization_version u32 = 2
llama_model_loader: - kv 44: general.file_type u32 = 7
llama_model_loader: - kv 45: quantize.imatrix.file str = Qwen3.5-27B-GGUF/imatrix_unsloth.gguf
llama_model_loader: - kv 46: quantize.imatrix.dataset str = unsloth_calibration_Qwen3.5-27B.txt
llama_model_loader: - kv 47: quantize.imatrix.entries_count u32 = 496
llama_model_loader: - kv 48: quantize.imatrix.chunks_count u32 = 80
llama_model_loader: - type f32: 353 tensors
llama_model_loader: - type q8_0: 498 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q8_0
print_info: file size = 26.62 GiB (8.50 BPW)
load: 0 unused tokens
load: printing all EOG tokens:
load: - 248044 ('<|endoftext|>')
load: - 248046 ('<|im_end|>')
load: - 248063 ('<|fim_pad|>')
load: - 248064 ('<|repo_name|>')
load: - 248065 ('<|file_sep|>')
load: special tokens cache size = 33
load: token to piece cache size = 1.7581 MB
print_info: arch = qwen35
print_info: vocab_only = 0
print_info: no_alloc = 0
print_info: n_ctx_train = 262144
print_info: n_embd = 5120
print_info: n_embd_inp = 5120
print_info: n_layer = 64
print_info: n_head = 24
print_info: n_head_kv = 4
print_info: n_rot = 64
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 256
print_info: n_embd_head_v = 256
print_info: n_gqa = 6
print_info: n_embd_k_gqa = 1024
print_info: n_embd_v_gqa = 1024
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: n_ff = 17408
print_info: n_expert = 0
print_info: n_expert_used = 0
print_info: n_expert_groups = 0
print_info: n_group_used = 0
print_info: causal attn = 1
print_info: pooling type = -1
print_info: rope type = 40
print_info: rope scaling = linear
print_info: freq_base_train = 10000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 262144
print_info: rope_yarn_log_mul = 0.0000
print_info: rope_finetuned = unknown
print_info: mrope sections = [11, 11, 10, 0]
print_info: ssm_d_conv = 4
print_info: ssm_d_inner = 6144
print_info: ssm_d_state = 128
print_info: ssm_dt_rank = 48
print_info: ssm_n_group = 16
print_info: ssm_dt_b_c_rms = 0
print_info: model type = 27B
print_info: model params = 26.90 B
print_info: general.name = Qwen3.5-27B
print_info: vocab type = BPE
print_info: n_vocab = 248320
print_info: n_merges = 247587
print_info: BOS token = 11 ','
print_info: EOS token = 248046 '<|im_end|>'
print_info: EOT token = 248046 '<|im_end|>'
print_info: PAD token = 248055 '<|vision_pad|>'
print_info: LF token = 198 'Ċ'
print_info: FIM PRE token = 248060 '<|fim_prefix|>'
print_info: FIM SUF token = 248062 '<|fim_suffix|>'
print_info: FIM MID token = 248061 '<|fim_middle|>'
print_info: FIM PAD token = 248063 '<|fim_pad|>'
print_info: FIM REP token = 248064 '<|repo_name|>'
print_info: FIM SEP token = 248065 '<|file_sep|>'
print_info: EOG token = 248044 '<|endoftext|>'
print_info: EOG token = 248046 '<|im_end|>'
print_info: EOG token = 248063 '<|fim_pad|>'
print_info: EOG token = 248064 '<|repo_name|>'
print_info: EOG token = 248065 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = true, direct_io = false)
load_tensors: offloading output layer to GPU
load_tensors: offloading 63 repeating layers to GPU
load_tensors: offloaded 65/65 layers to GPU
load_tensors: CPU_Mapped model buffer size = 1288.28 MiB
load_tensors: MTL0_Mapped model buffer size = 27260.56 MiB
.............................................................................................
common_init_result: added <|endoftext|> logit bias = -inf
common_init_result: added <|im_end|> logit bias = -inf
common_init_result: added <|fim_pad|> logit bias = -inf
common_init_result: added <|repo_name|> logit bias = -inf
common_init_result: added <|file_sep|> logit bias = -inf
llama_context: constructing llama_context
llama_context: n_seq_max = 4
llama_context: n_ctx = 2048
llama_context: n_ctx_seq = 512
llama_context: n_batch = 2048
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = enabled
llama_context: kv_unified = false
llama_context: freq_base = 10000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_seq (512) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M5 Max
ggml_metal_init: picking default device: Apple M5 Max
ggml_metal_init: use fusion = true
ggml_metal_init: use concurrency = true
ggml_metal_init: use graph optimize = true
llama_context: CPU output buffer size = 3.79 MiB
llama_kv_cache: MTL0 KV buffer size = 28.12 MiB
llama_kv_cache: TurboQuant rotation matrices initialized (128x128)
llama_kv_cache: size = 28.00 MiB ( 512 cells, 16 layers, 4/4 seqs), K (turbo3): 14.00 MiB, V (turbo3): 14.00 MiB
llama_memory_recurrent: MTL0 RS buffer size = 598.50 MiB
llama_memory_recurrent: size = 598.50 MiB ( 4 cells, 64 layers, 4 seqs), R (f32): 22.50 MiB, S (f32): 576.00 MiB
sched_reserve: reserving ...
sched_reserve: resolving fused Gated Delta Net support:
sched_reserve: fused Gated Delta Net (autoregressive) enabled
sched_reserve: fused Gated Delta Net (chunked) enabled
sched_reserve: MTL0 compute buffer size = 495.00 MiB
sched_reserve: CPU compute buffer size = 21.02 MiB
sched_reserve: graph nodes = 3721
sched_reserve: graph splits = 2
sched_reserve: reserve took 24.66 ms, sched copies = 1
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
system_info: n_threads = 6 (n_threads_batch = 6) / 18 | MTL : EMBED_LIBRARY = 1 | CPU : NEON = 1 | ARM_FMA = 1 | FP16_VA = 1 | MATMUL_INT8 = 1 | DOTPROD = 1 | SME = 1 | ACCELERATE = 1 | REPACK = 1 |
kl_divergence: computing over 8 chunks, n_ctx=512, batch_size=2048, n_seq=4
kl_divergence: 4.28 seconds per pass - ETA 0.13 minutes
chunk PPL ln(PPL(Q)/PPL(base)) KL Divergence Δp RMS Same top p
1 4.3891 ± 0.6285 0.00023 ± 0.00865 0.01188 ± 0.00508 2.850 ± 0.644 % 96.078 ± 1.218 %
2 6.2904 ± 0.7414 0.01348 ± 0.00709 0.01128 ± 0.00258 2.789 ± 0.366 % 96.275 ± 0.839 %
3 6.3143 ± 0.6098 0.02357 ± 0.00670 0.01117 ± 0.00178 2.908 ± 0.264 % 96.078 ± 0.702 %
4 6.3391 ± 0.5110 0.02657 ± 0.00608 0.01129 ± 0.00143 2.992 ± 0.228 % 95.686 ± 0.636 %
5 6.2373 ± 0.4343 0.02519 ± 0.00517 0.01070 ± 0.00117 2.887 ± 0.193 % 95.765 ± 0.564 %
6 6.4284 ± 0.4061 0.02143 ± 0.00454 0.01002 ± 0.00098 2.769 ± 0.170 % 95.882 ± 0.508 %
7 6.5377 ± 0.3789 0.01993 ± 0.00413 0.00973 ± 0.00085 2.823 ± 0.166 % 96.022 ± 0.463 %
8 7.0066 ± 0.3904 0.01927 ± 0.00378 0.00990 ± 0.00087 2.738 ± 0.152 % 95.980 ± 0.435 %
====== Perplexity statistics ======
Mean PPL(Q) : 7.006588 ± 0.390413
Mean PPL(base) : 6.872893 ± 0.379370
Cor(ln(PPL(Q)), ln(PPL(base))): 99.77%
Mean ln(PPL(Q)/PPL(base)) : 0.019266 ± 0.003778
Mean PPL(Q)/PPL(base) : 1.019453 ± 0.003851
Mean PPL(Q)-PPL(base) : 0.133695 ± 0.028215
====== KL divergence statistics ======
Mean KLD: 0.009900 ± 0.000868
Maximum KLD: 1.290557
99.9% KLD: 0.380763
99.0% KLD: 0.074194
95.0% KLD: 0.027761
90.0% KLD: 0.018358
Median KLD: 0.004675
10.0% KLD: 0.000291
5.0% KLD: 0.000093
1.0% KLD: 0.000008
0.1% KLD: 0.000000
Minimum KLD: -0.000000
====== Token probability statistics ======
Mean Δp: -0.237 ± 0.060 %
Maximum Δp: 30.386%
99.9% Δp: 16.228%
99.0% Δp: 7.073%
95.0% Δp: 3.093%
90.0% Δp: 1.901%
75.0% Δp: 0.384%
Median Δp: -0.013%
25.0% Δp: -0.743%
10.0% Δp: -2.420%
5.0% Δp: -4.054%
1.0% Δp: -9.246%
0.1% Δp: -21.460%
Minimum Δp: -23.586%
RMS Δp : 2.738 ± 0.152 %
Same top p: 95.980 ± 0.435 %
llama_perf_context_print: load time = 1900.16 ms
llama_perf_context_print: prompt eval time = 4950.38 ms / 4096 tokens ( 1.21 ms per token, 827.41 tokens per second)
llama_perf_context_print: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
llama_perf_context_print: total time = 9575.86 ms / 4097 tokens
llama_perf_context_print: graphs reused = 2
llama_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted |
llama_memory_breakdown_print: | - MTL0 (Apple M5 Max) | 110100 = 81717 + (28382 = 27260 + 626 + 495) + 0 |
llama_memory_breakdown_print: | - Host | 1309 = 1288 + 0 + 21 |
ggml_metal_free: deallocating
benchmark-results-raw/kld_moe_q4_0.log
+273
View File
@@ -0,0 +1,273 @@
ggml_metal_device_init: testing tensor API for f16 support
ggml_metal_library_compile_pipeline: compiling pipeline: base = 'dummy_kernel', name = 'dummy_kernel'
ggml_metal_library_compile_pipeline: loaded dummy_kernel 0x1018627f0 | th_max = 1024 | th_width = 32
ggml_metal_device_init: testing tensor API for bfloat support
ggml_metal_library_compile_pipeline: compiling pipeline: base = 'dummy_kernel', name = 'dummy_kernel'
ggml_metal_library_compile_pipeline: loaded dummy_kernel 0x101862470 | th_max = 1024 | th_width = 32
ggml_metal_library_init: using embedded metal library
ggml_metal_library_init: turbo3 sparse V dequant enabled
ggml_metal_library_init: loaded in 0.009 sec
ggml_metal_rsets_init: creating a residency set collection (keep_alive = 180 s)
ggml_metal_device_init: GPU name: MTL0
ggml_metal_device_init: GPU family: MTLGPUFamilyApple10 (1010)
ggml_metal_device_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_device_init: GPU family: MTLGPUFamilyMetal4 (5002)
ggml_metal_device_init: simdgroup reduction = true
ggml_metal_device_init: simdgroup matrix mul. = true
ggml_metal_device_init: has unified memory = true
ggml_metal_device_init: has bfloat = true
ggml_metal_device_init: has tensor = true
ggml_metal_device_init: use residency sets = true
ggml_metal_device_init: use shared buffers = true
ggml_metal_device_init: recommendedMaxWorkingSetSize = 115448.73 MB
build: 159 (7d1bd95) with AppleClang 17.0.0.17000604 for Darwin arm64
common_init_result: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on
llama_params_fit_impl: projected to use 35423 MiB of device memory vs. 109848 MiB of free device memory
llama_params_fit_impl: will leave 74425 >= 1024 MiB of free device memory, no changes needed
llama_params_fit: successfully fit params to free device memory
llama_params_fit: fitting params to free memory took 0.25 seconds
llama_model_load_from_file_impl: using device MTL0 (Apple M5 Max) (unknown id) - 110100 MiB free
llama_model_loader: loaded meta data with 52 key-value pairs and 733 tensors from /Users/tom/local_llms/models/Qwen3.5-35B-A3B-Q8_0.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = qwen35moe
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.sampling.top_k i32 = 20
llama_model_loader: - kv 3: general.sampling.top_p f32 = 0.950000
llama_model_loader: - kv 4: general.sampling.temp f32 = 1.000000
llama_model_loader: - kv 5: general.name str = Qwen3.5-35B-A3B
llama_model_loader: - kv 6: general.basename str = Qwen3.5-35B-A3B
llama_model_loader: - kv 7: general.quantized_by str = Unsloth
llama_model_loader: - kv 8: general.size_label str = 35B-A3B
llama_model_loader: - kv 9: general.license str = apache-2.0
llama_model_loader: - kv 10: general.license.link str = https://huggingface.co/Qwen/Qwen3.5-3...
llama_model_loader: - kv 11: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 12: general.base_model.count u32 = 1
llama_model_loader: - kv 13: general.base_model.0.name str = Qwen3.5 35B A3B
llama_model_loader: - kv 14: general.base_model.0.organization str = Qwen
llama_model_loader: - kv 15: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen3.5-3...
llama_model_loader: - kv 16: general.tags arr[str,2] = ["unsloth", "image-text-to-text"]
llama_model_loader: - kv 17: qwen35moe.block_count u32 = 40
llama_model_loader: - kv 18: qwen35moe.context_length u32 = 262144
llama_model_loader: - kv 19: qwen35moe.embedding_length u32 = 2048
llama_model_loader: - kv 20: qwen35moe.attention.head_count u32 = 16
llama_model_loader: - kv 21: qwen35moe.attention.head_count_kv u32 = 2
llama_model_loader: - kv 22: qwen35moe.rope.dimension_sections arr[i32,4] = [11, 11, 10, 0]
llama_model_loader: - kv 23: qwen35moe.rope.freq_base f32 = 10000000.000000
llama_model_loader: - kv 24: qwen35moe.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 25: qwen35moe.expert_count u32 = 256
llama_model_loader: - kv 26: qwen35moe.expert_used_count u32 = 8
llama_model_loader: - kv 27: qwen35moe.attention.key_length u32 = 256
llama_model_loader: - kv 28: qwen35moe.attention.value_length u32 = 256
llama_model_loader: - kv 29: qwen35moe.expert_feed_forward_length u32 = 512
llama_model_loader: - kv 30: qwen35moe.expert_shared_feed_forward_length u32 = 512
llama_model_loader: - kv 31: qwen35moe.ssm.conv_kernel u32 = 4
llama_model_loader: - kv 32: qwen35moe.ssm.state_size u32 = 128
llama_model_loader: - kv 33: qwen35moe.ssm.group_count u32 = 16
llama_model_loader: - kv 34: qwen35moe.ssm.time_step_rank u32 = 32
llama_model_loader: - kv 35: qwen35moe.ssm.inner_size u32 = 4096
llama_model_loader: - kv 36: qwen35moe.full_attention_interval u32 = 4
llama_model_loader: - kv 37: qwen35moe.rope.dimension_count u32 = 64
llama_model_loader: - kv 38: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 39: tokenizer.ggml.pre str = qwen35
llama_model_loader: - kv 40: tokenizer.ggml.tokens arr[str,248320] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 41: tokenizer.ggml.token_type arr[i32,248320] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 42: tokenizer.ggml.merges arr[str,247587] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 43: tokenizer.ggml.eos_token_id u32 = 248046
llama_model_loader: - kv 44: tokenizer.ggml.padding_token_id u32 = 248055
llama_model_loader: - kv 45: tokenizer.chat_template str = {%- set image_count = namespace(value...
llama_model_loader: - kv 46: general.quantization_version u32 = 2
llama_model_loader: - kv 47: general.file_type u32 = 7
llama_model_loader: - kv 48: quantize.imatrix.file str = Qwen3.5-35B-A3B-GGUF/imatrix_unsloth....
llama_model_loader: - kv 49: quantize.imatrix.dataset str = unsloth_calibration_Qwen3.5-35B-A3B.txt
llama_model_loader: - kv 50: quantize.imatrix.entries_count u32 = 510
llama_model_loader: - kv 51: quantize.imatrix.chunks_count u32 = 76
llama_model_loader: - type f32: 301 tensors
llama_model_loader: - type q8_0: 432 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q8_0
print_info: file size = 34.36 GiB (8.52 BPW)
load: 0 unused tokens
load: printing all EOG tokens:
load: - 248044 ('<|endoftext|>')
load: - 248046 ('<|im_end|>')
load: - 248063 ('<|fim_pad|>')
load: - 248064 ('<|repo_name|>')
load: - 248065 ('<|file_sep|>')
load: special tokens cache size = 33
load: token to piece cache size = 1.7581 MB
print_info: arch = qwen35moe
print_info: vocab_only = 0
print_info: no_alloc = 0
print_info: n_ctx_train = 262144
print_info: n_embd = 2048
print_info: n_embd_inp = 2048
print_info: n_layer = 40
print_info: n_head = 16
print_info: n_head_kv = 2
print_info: n_rot = 64
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 256
print_info: n_embd_head_v = 256
print_info: n_gqa = 8
print_info: n_embd_k_gqa = 512
print_info: n_embd_v_gqa = 512
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: n_ff = 0
print_info: n_expert = 256
print_info: n_expert_used = 8
print_info: n_expert_groups = 0
print_info: n_group_used = 0
print_info: causal attn = 1
print_info: pooling type = -1
print_info: rope type = 40
print_info: rope scaling = linear
print_info: freq_base_train = 10000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 262144
print_info: rope_yarn_log_mul = 0.0000
print_info: rope_finetuned = unknown
print_info: mrope sections = [11, 11, 10, 0]
print_info: ssm_d_conv = 4
print_info: ssm_d_inner = 4096
print_info: ssm_d_state = 128
print_info: ssm_dt_rank = 32
print_info: ssm_n_group = 16
print_info: ssm_dt_b_c_rms = 0
print_info: model type = 35B.A3B
print_info: model params = 34.66 B
print_info: general.name = Qwen3.5-35B-A3B
print_info: vocab type = BPE
print_info: n_vocab = 248320
print_info: n_merges = 247587
print_info: BOS token = 11 ','
print_info: EOS token = 248046 '<|im_end|>'
print_info: EOT token = 248046 '<|im_end|>'
print_info: PAD token = 248055 '<|vision_pad|>'
print_info: LF token = 198 'Ċ'
print_info: FIM PRE token = 248060 '<|fim_prefix|>'
print_info: FIM SUF token = 248062 '<|fim_suffix|>'
print_info: FIM MID token = 248061 '<|fim_middle|>'
print_info: FIM PAD token = 248063 '<|fim_pad|>'
print_info: FIM REP token = 248064 '<|repo_name|>'
print_info: FIM SEP token = 248065 '<|file_sep|>'
print_info: EOG token = 248044 '<|endoftext|>'
print_info: EOG token = 248046 '<|im_end|>'
print_info: EOG token = 248063 '<|fim_pad|>'
print_info: EOG token = 248064 '<|repo_name|>'
print_info: EOG token = 248065 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = true, direct_io = false)
load_tensors: offloading output layer to GPU
load_tensors: offloading 39 repeating layers to GPU
load_tensors: offloaded 41/41 layers to GPU
load_tensors: CPU_Mapped model buffer size = 515.31 MiB
load_tensors: MTL0_Mapped model buffer size = 35183.10 MiB
....................................................................................................
common_init_result: added <|endoftext|> logit bias = -inf
common_init_result: added <|im_end|> logit bias = -inf
common_init_result: added <|fim_pad|> logit bias = -inf
common_init_result: added <|repo_name|> logit bias = -inf
common_init_result: added <|file_sep|> logit bias = -inf
llama_context: constructing llama_context
llama_context: n_seq_max = 4
llama_context: n_ctx = 2048
llama_context: n_ctx_seq = 512
llama_context: n_batch = 2048
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = enabled
llama_context: kv_unified = false
llama_context: freq_base = 10000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_seq (512) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M5 Max
ggml_metal_init: picking default device: Apple M5 Max
ggml_metal_init: use fusion = true
ggml_metal_init: use concurrency = true
ggml_metal_init: use graph optimize = true
llama_context: CPU output buffer size = 3.79 MiB
llama_kv_cache: MTL0 KV buffer size = 11.25 MiB
llama_kv_cache: size = 11.25 MiB ( 512 cells, 10 layers, 4/4 seqs), K (q4_0): 5.62 MiB, V (q4_0): 5.62 MiB
llama_memory_recurrent: MTL0 RS buffer size = 251.25 MiB
llama_memory_recurrent: size = 251.25 MiB ( 4 cells, 40 layers, 4 seqs), R (f32): 11.25 MiB, S (f32): 240.00 MiB
sched_reserve: reserving ...
sched_reserve: resolving fused Gated Delta Net support:
sched_reserve: fused Gated Delta Net (autoregressive) enabled
sched_reserve: fused Gated Delta Net (chunked) enabled
sched_reserve: MTL0 compute buffer size = 493.00 MiB
sched_reserve: CPU compute buffer size = 9.02 MiB
sched_reserve: graph nodes = 3749
sched_reserve: graph splits = 2
sched_reserve: reserve took 22.20 ms, sched copies = 1
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
system_info: n_threads = 6 (n_threads_batch = 6) / 18 | MTL : EMBED_LIBRARY = 1 | CPU : NEON = 1 | ARM_FMA = 1 | FP16_VA = 1 | MATMUL_INT8 = 1 | DOTPROD = 1 | SME = 1 | ACCELERATE = 1 | REPACK = 1 |
kl_divergence: computing over 8 chunks, n_ctx=512, batch_size=2048, n_seq=4
kl_divergence: 0.94 seconds per pass - ETA 0.02 minutes
chunk PPL ln(PPL(Q)/PPL(base)) KL Divergence Δp RMS Same top p
1 4.2535 ± 0.5938 0.01064 ± 0.00834 0.00602 ± 0.00075 2.204 ± 0.222 % 98.039 ± 0.870 %
2 6.0305 ± 0.7005 0.00343 ± 0.00550 0.00678 ± 0.00053 2.319 ± 0.226 % 96.275 ± 0.839 %
3 5.1194 ± 0.4644 0.00627 ± 0.00491 0.00836 ± 0.00051 2.896 ± 0.209 % 96.209 ± 0.691 %
4 5.0906 ± 0.3783 0.00346 ± 0.00412 0.00791 ± 0.00041 2.788 ± 0.175 % 95.784 ± 0.629 %
5 5.2499 ± 0.3424 0.00199 ± 0.00372 0.00799 ± 0.00039 2.847 ± 0.186 % 95.686 ± 0.569 %
6 5.4190 ± 0.3215 0.00031 ± 0.00338 0.00797 ± 0.00034 2.801 ± 0.163 % 95.621 ± 0.523 %
7 5.6227 ± 0.3100 0.00333 ± 0.00339 0.00798 ± 0.00031 2.715 ± 0.146 % 95.742 ± 0.478 %
8 6.1424 ± 0.3270 0.00436 ± 0.00313 0.00809 ± 0.00029 2.753 ± 0.138 % 95.833 ± 0.443 %
====== Perplexity statistics ======
Mean PPL(Q) : 6.142449 ± 0.327049
Mean PPL(base) : 6.115704 ± 0.325284
Cor(ln(PPL(Q)), ln(PPL(base))): 99.83%
Mean ln(PPL(Q)/PPL(base)) : 0.004364 ± 0.003129
Mean PPL(Q)/PPL(base) : 1.004373 ± 0.003143
Mean PPL(Q)-PPL(base) : 0.026745 ± 0.019256
====== KL divergence statistics ======
Mean KLD: 0.008091 ± 0.000292
Maximum KLD: 0.228670
99.9% KLD: 0.133784
99.0% KLD: 0.063379
95.0% KLD: 0.027492
90.0% KLD: 0.018377
Median KLD: 0.004403
10.0% KLD: 0.000252
5.0% KLD: 0.000082
1.0% KLD: 0.000015
0.1% KLD: 0.000002
Minimum KLD: -0.000002
====== Token probability statistics ======
Mean Δp: -0.171 ± 0.061 %
Maximum Δp: 20.505%
99.9% Δp: 16.916%
99.0% Δp: 8.096%
95.0% Δp: 3.384%
90.0% Δp: 2.151%
75.0% Δp: 0.464%
Median Δp: -0.008%
25.0% Δp: -0.737%
10.0% Δp: -2.572%
5.0% Δp: -4.112%
1.0% Δp: -9.084%
0.1% Δp: -19.815%
Minimum Δp: -29.275%
RMS Δp : 2.753 ± 0.138 %
Same top p: 95.833 ± 0.443 %
llama_perf_context_print: load time = 2306.32 ms
llama_perf_context_print: prompt eval time = 843.12 ms / 4096 tokens ( 0.21 ms per token, 4858.17 tokens per second)
llama_perf_context_print: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
llama_perf_context_print: total time = 2147.86 ms / 4097 tokens
llama_perf_context_print: graphs reused = 2
llama_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted |
llama_memory_breakdown_print: | - MTL0 (Apple M5 Max) | 110100 = 74161 + (35938 = 35183 + 262 + 493) + 0 |
llama_memory_breakdown_print: | - Host | 524 = 515 + 0 + 9 |
ggml_metal_free: deallocating
benchmark-results-raw/kld_moe_q8_0.log
+273
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@@ -0,0 +1,273 @@
ggml_metal_device_init: testing tensor API for f16 support
ggml_metal_library_compile_pipeline: compiling pipeline: base = 'dummy_kernel', name = 'dummy_kernel'
ggml_metal_library_compile_pipeline: loaded dummy_kernel 0x10655a080 | th_max = 1024 | th_width = 32
ggml_metal_device_init: testing tensor API for bfloat support
ggml_metal_library_compile_pipeline: compiling pipeline: base = 'dummy_kernel', name = 'dummy_kernel'
ggml_metal_library_compile_pipeline: loaded dummy_kernel 0x106559d00 | th_max = 1024 | th_width = 32
ggml_metal_library_init: using embedded metal library
ggml_metal_library_init: turbo3 sparse V dequant enabled
ggml_metal_library_init: loaded in 0.007 sec
ggml_metal_rsets_init: creating a residency set collection (keep_alive = 180 s)
ggml_metal_device_init: GPU name: MTL0
ggml_metal_device_init: GPU family: MTLGPUFamilyApple10 (1010)
ggml_metal_device_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_device_init: GPU family: MTLGPUFamilyMetal4 (5002)
ggml_metal_device_init: simdgroup reduction = true
ggml_metal_device_init: simdgroup matrix mul. = true
ggml_metal_device_init: has unified memory = true
ggml_metal_device_init: has bfloat = true
ggml_metal_device_init: has tensor = true
ggml_metal_device_init: use residency sets = true
ggml_metal_device_init: use shared buffers = true
ggml_metal_device_init: recommendedMaxWorkingSetSize = 115448.73 MB
build: 159 (7d1bd95) with AppleClang 17.0.0.17000604 for Darwin arm64
common_init_result: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on
llama_params_fit_impl: projected to use 35433 MiB of device memory vs. 109848 MiB of free device memory
llama_params_fit_impl: will leave 74415 >= 1024 MiB of free device memory, no changes needed
llama_params_fit: successfully fit params to free device memory
llama_params_fit: fitting params to free memory took 0.25 seconds
llama_model_load_from_file_impl: using device MTL0 (Apple M5 Max) (unknown id) - 110100 MiB free
llama_model_loader: loaded meta data with 52 key-value pairs and 733 tensors from /Users/tom/local_llms/models/Qwen3.5-35B-A3B-Q8_0.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = qwen35moe
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.sampling.top_k i32 = 20
llama_model_loader: - kv 3: general.sampling.top_p f32 = 0.950000
llama_model_loader: - kv 4: general.sampling.temp f32 = 1.000000
llama_model_loader: - kv 5: general.name str = Qwen3.5-35B-A3B
llama_model_loader: - kv 6: general.basename str = Qwen3.5-35B-A3B
llama_model_loader: - kv 7: general.quantized_by str = Unsloth
llama_model_loader: - kv 8: general.size_label str = 35B-A3B
llama_model_loader: - kv 9: general.license str = apache-2.0
llama_model_loader: - kv 10: general.license.link str = https://huggingface.co/Qwen/Qwen3.5-3...
llama_model_loader: - kv 11: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 12: general.base_model.count u32 = 1
llama_model_loader: - kv 13: general.base_model.0.name str = Qwen3.5 35B A3B
llama_model_loader: - kv 14: general.base_model.0.organization str = Qwen
llama_model_loader: - kv 15: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen3.5-3...
llama_model_loader: - kv 16: general.tags arr[str,2] = ["unsloth", "image-text-to-text"]
llama_model_loader: - kv 17: qwen35moe.block_count u32 = 40
llama_model_loader: - kv 18: qwen35moe.context_length u32 = 262144
llama_model_loader: - kv 19: qwen35moe.embedding_length u32 = 2048
llama_model_loader: - kv 20: qwen35moe.attention.head_count u32 = 16
llama_model_loader: - kv 21: qwen35moe.attention.head_count_kv u32 = 2
llama_model_loader: - kv 22: qwen35moe.rope.dimension_sections arr[i32,4] = [11, 11, 10, 0]
llama_model_loader: - kv 23: qwen35moe.rope.freq_base f32 = 10000000.000000
llama_model_loader: - kv 24: qwen35moe.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 25: qwen35moe.expert_count u32 = 256
llama_model_loader: - kv 26: qwen35moe.expert_used_count u32 = 8
llama_model_loader: - kv 27: qwen35moe.attention.key_length u32 = 256
llama_model_loader: - kv 28: qwen35moe.attention.value_length u32 = 256
llama_model_loader: - kv 29: qwen35moe.expert_feed_forward_length u32 = 512
llama_model_loader: - kv 30: qwen35moe.expert_shared_feed_forward_length u32 = 512
llama_model_loader: - kv 31: qwen35moe.ssm.conv_kernel u32 = 4
llama_model_loader: - kv 32: qwen35moe.ssm.state_size u32 = 128
llama_model_loader: - kv 33: qwen35moe.ssm.group_count u32 = 16
llama_model_loader: - kv 34: qwen35moe.ssm.time_step_rank u32 = 32
llama_model_loader: - kv 35: qwen35moe.ssm.inner_size u32 = 4096
llama_model_loader: - kv 36: qwen35moe.full_attention_interval u32 = 4
llama_model_loader: - kv 37: qwen35moe.rope.dimension_count u32 = 64
llama_model_loader: - kv 38: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 39: tokenizer.ggml.pre str = qwen35
llama_model_loader: - kv 40: tokenizer.ggml.tokens arr[str,248320] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 41: tokenizer.ggml.token_type arr[i32,248320] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 42: tokenizer.ggml.merges arr[str,247587] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 43: tokenizer.ggml.eos_token_id u32 = 248046
llama_model_loader: - kv 44: tokenizer.ggml.padding_token_id u32 = 248055
llama_model_loader: - kv 45: tokenizer.chat_template str = {%- set image_count = namespace(value...
llama_model_loader: - kv 46: general.quantization_version u32 = 2
llama_model_loader: - kv 47: general.file_type u32 = 7
llama_model_loader: - kv 48: quantize.imatrix.file str = Qwen3.5-35B-A3B-GGUF/imatrix_unsloth....
llama_model_loader: - kv 49: quantize.imatrix.dataset str = unsloth_calibration_Qwen3.5-35B-A3B.txt
llama_model_loader: - kv 50: quantize.imatrix.entries_count u32 = 510
llama_model_loader: - kv 51: quantize.imatrix.chunks_count u32 = 76
llama_model_loader: - type f32: 301 tensors
llama_model_loader: - type q8_0: 432 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q8_0
print_info: file size = 34.36 GiB (8.52 BPW)
load: 0 unused tokens
load: printing all EOG tokens:
load: - 248044 ('<|endoftext|>')
load: - 248046 ('<|im_end|>')
load: - 248063 ('<|fim_pad|>')
load: - 248064 ('<|repo_name|>')
load: - 248065 ('<|file_sep|>')
load: special tokens cache size = 33
load: token to piece cache size = 1.7581 MB
print_info: arch = qwen35moe
print_info: vocab_only = 0
print_info: no_alloc = 0
print_info: n_ctx_train = 262144
print_info: n_embd = 2048
print_info: n_embd_inp = 2048
print_info: n_layer = 40
print_info: n_head = 16
print_info: n_head_kv = 2
print_info: n_rot = 64
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 256
print_info: n_embd_head_v = 256
print_info: n_gqa = 8
print_info: n_embd_k_gqa = 512
print_info: n_embd_v_gqa = 512
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: n_ff = 0
print_info: n_expert = 256
print_info: n_expert_used = 8
print_info: n_expert_groups = 0
print_info: n_group_used = 0
print_info: causal attn = 1
print_info: pooling type = -1
print_info: rope type = 40
print_info: rope scaling = linear
print_info: freq_base_train = 10000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 262144
print_info: rope_yarn_log_mul = 0.0000
print_info: rope_finetuned = unknown
print_info: mrope sections = [11, 11, 10, 0]
print_info: ssm_d_conv = 4
print_info: ssm_d_inner = 4096
print_info: ssm_d_state = 128
print_info: ssm_dt_rank = 32
print_info: ssm_n_group = 16
print_info: ssm_dt_b_c_rms = 0
print_info: model type = 35B.A3B
print_info: model params = 34.66 B
print_info: general.name = Qwen3.5-35B-A3B
print_info: vocab type = BPE
print_info: n_vocab = 248320
print_info: n_merges = 247587
print_info: BOS token = 11 ','
print_info: EOS token = 248046 '<|im_end|>'
print_info: EOT token = 248046 '<|im_end|>'
print_info: PAD token = 248055 '<|vision_pad|>'
print_info: LF token = 198 'Ċ'
print_info: FIM PRE token = 248060 '<|fim_prefix|>'
print_info: FIM SUF token = 248062 '<|fim_suffix|>'
print_info: FIM MID token = 248061 '<|fim_middle|>'
print_info: FIM PAD token = 248063 '<|fim_pad|>'
print_info: FIM REP token = 248064 '<|repo_name|>'
print_info: FIM SEP token = 248065 '<|file_sep|>'
print_info: EOG token = 248044 '<|endoftext|>'
print_info: EOG token = 248046 '<|im_end|>'
print_info: EOG token = 248063 '<|fim_pad|>'
print_info: EOG token = 248064 '<|repo_name|>'
print_info: EOG token = 248065 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = true, direct_io = false)
load_tensors: offloading output layer to GPU
load_tensors: offloading 39 repeating layers to GPU
load_tensors: offloaded 41/41 layers to GPU
load_tensors: CPU_Mapped model buffer size = 515.31 MiB
load_tensors: MTL0_Mapped model buffer size = 35183.10 MiB
....................................................................................................
common_init_result: added <|endoftext|> logit bias = -inf
common_init_result: added <|im_end|> logit bias = -inf
common_init_result: added <|fim_pad|> logit bias = -inf
common_init_result: added <|repo_name|> logit bias = -inf
common_init_result: added <|file_sep|> logit bias = -inf
llama_context: constructing llama_context
llama_context: n_seq_max = 4
llama_context: n_ctx = 2048
llama_context: n_ctx_seq = 512
llama_context: n_batch = 2048
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = enabled
llama_context: kv_unified = false
llama_context: freq_base = 10000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_seq (512) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M5 Max
ggml_metal_init: picking default device: Apple M5 Max
ggml_metal_init: use fusion = true
ggml_metal_init: use concurrency = true
ggml_metal_init: use graph optimize = true
llama_context: CPU output buffer size = 3.79 MiB
llama_kv_cache: MTL0 KV buffer size = 21.25 MiB
llama_kv_cache: size = 21.25 MiB ( 512 cells, 10 layers, 4/4 seqs), K (q8_0): 10.62 MiB, V (q8_0): 10.62 MiB
llama_memory_recurrent: MTL0 RS buffer size = 251.25 MiB
llama_memory_recurrent: size = 251.25 MiB ( 4 cells, 40 layers, 4 seqs), R (f32): 11.25 MiB, S (f32): 240.00 MiB
sched_reserve: reserving ...
sched_reserve: resolving fused Gated Delta Net support:
sched_reserve: fused Gated Delta Net (autoregressive) enabled
sched_reserve: fused Gated Delta Net (chunked) enabled
sched_reserve: MTL0 compute buffer size = 493.00 MiB
sched_reserve: CPU compute buffer size = 9.02 MiB
sched_reserve: graph nodes = 3749
sched_reserve: graph splits = 2
sched_reserve: reserve took 21.43 ms, sched copies = 1
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
system_info: n_threads = 6 (n_threads_batch = 6) / 18 | MTL : EMBED_LIBRARY = 1 | CPU : NEON = 1 | ARM_FMA = 1 | FP16_VA = 1 | MATMUL_INT8 = 1 | DOTPROD = 1 | SME = 1 | ACCELERATE = 1 | REPACK = 1 |
kl_divergence: computing over 8 chunks, n_ctx=512, batch_size=2048, n_seq=4
kl_divergence: 0.90 seconds per pass - ETA 0.02 minutes
chunk PPL ln(PPL(Q)/PPL(base)) KL Divergence Δp RMS Same top p
1 4.2378 ± 0.5914 0.00692 ± 0.00306 0.00103 ± 0.00015 1.014 ± 0.152 % 98.824 ± 0.677 %
2 6.0377 ± 0.7022 0.00464 ± 0.00219 0.00121 ± 0.00024 1.324 ± 0.367 % 98.431 ± 0.551 %
3 5.0850 ± 0.4606 -0.00047 ± 0.00238 0.00202 ± 0.00024 1.601 ± 0.235 % 98.301 ± 0.468 %
4 5.0680 ± 0.3763 -0.00097 ± 0.00195 0.00182 ± 0.00020 1.482 ± 0.193 % 98.431 ± 0.389 %
5 5.2338 ± 0.3417 -0.00107 ± 0.00167 0.00170 ± 0.00016 1.362 ± 0.168 % 98.353 ± 0.357 %
6 5.4076 ± 0.3215 -0.00179 ± 0.00150 0.00164 ± 0.00014 1.292 ± 0.148 % 98.431 ± 0.318 %
7 5.5998 ± 0.3089 -0.00073 ± 0.00171 0.00159 ± 0.00012 1.252 ± 0.132 % 98.431 ± 0.294 %
8 6.1109 ± 0.3255 -0.00078 ± 0.00160 0.00155 ± 0.00011 1.231 ± 0.119 % 98.431 ± 0.275 %
====== Perplexity statistics ======
Mean PPL(Q) : 6.110915 ± 0.325525
Mean PPL(base) : 6.115704 ± 0.325284
Cor(ln(PPL(Q)), ln(PPL(base))): 99.96%
Mean ln(PPL(Q)/PPL(base)) : -0.000783 ± 0.001599
Mean PPL(Q)/PPL(base) : 0.999217 ± 0.001598
Mean PPL(Q)-PPL(base) : -0.004789 ± 0.009765
====== KL divergence statistics ======
Mean KLD: 0.001549 ± 0.000108
Maximum KLD: 0.111541
99.9% KLD: 0.060302
99.0% KLD: 0.019415
95.0% KLD: 0.005553
90.0% KLD: 0.003112
Median KLD: 0.000425
10.0% KLD: 0.000026
5.0% KLD: 0.000007
1.0% KLD: -0.000000
0.1% KLD: -0.000008
Minimum KLD: -0.000016
====== Token probability statistics ======
Mean Δp: 0.032 ± 0.027 %
Maximum Δp: 14.606%
99.9% Δp: 10.198%
99.0% Δp: 3.727%
95.0% Δp: 1.448%
90.0% Δp: 0.746%
75.0% Δp: 0.179%
Median Δp: -0.001%
25.0% Δp: -0.164%
10.0% Δp: -0.646%
5.0% Δp: -1.216%
1.0% Δp: -3.295%
0.1% Δp: -7.738%
Minimum Δp: -22.079%
RMS Δp : 1.231 ± 0.119 %
Same top p: 98.431 ± 0.275 %
llama_perf_context_print: load time = 2303.16 ms
llama_perf_context_print: prompt eval time = 837.87 ms / 4096 tokens ( 0.20 ms per token, 4888.57 tokens per second)
llama_perf_context_print: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
llama_perf_context_print: total time = 2089.41 ms / 4097 tokens
llama_perf_context_print: graphs reused = 2
llama_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted |
llama_memory_breakdown_print: | - MTL0 (Apple M5 Max) | 110100 = 74151 + (35948 = 35183 + 272 + 493) + 0 |
llama_memory_breakdown_print: | - Host | 524 = 515 + 0 + 9 |
ggml_metal_free: deallocating
benchmark-results-raw/kld_moe_turbo3.log
+274
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@@ -0,0 +1,274 @@
ggml_metal_device_init: testing tensor API for f16 support
ggml_metal_library_compile_pipeline: compiling pipeline: base = 'dummy_kernel', name = 'dummy_kernel'
ggml_metal_library_compile_pipeline: loaded dummy_kernel 0x1058cecf0 | th_max = 1024 | th_width = 32
ggml_metal_device_init: testing tensor API for bfloat support
ggml_metal_library_compile_pipeline: compiling pipeline: base = 'dummy_kernel', name = 'dummy_kernel'
ggml_metal_library_compile_pipeline: loaded dummy_kernel 0x1058ce970 | th_max = 1024 | th_width = 32
ggml_metal_library_init: using embedded metal library
ggml_metal_library_init: turbo3 sparse V dequant enabled
ggml_metal_library_init: loaded in 0.008 sec
ggml_metal_rsets_init: creating a residency set collection (keep_alive = 180 s)
ggml_metal_device_init: GPU name: MTL0
ggml_metal_device_init: GPU family: MTLGPUFamilyApple10 (1010)
ggml_metal_device_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_device_init: GPU family: MTLGPUFamilyMetal4 (5002)
ggml_metal_device_init: simdgroup reduction = true
ggml_metal_device_init: simdgroup matrix mul. = true
ggml_metal_device_init: has unified memory = true
ggml_metal_device_init: has bfloat = true
ggml_metal_device_init: has tensor = true
ggml_metal_device_init: use residency sets = true
ggml_metal_device_init: use shared buffers = true
ggml_metal_device_init: recommendedMaxWorkingSetSize = 115448.73 MB
build: 159 (7d1bd95) with AppleClang 17.0.0.17000604 for Darwin arm64
common_init_result: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on
llama_params_fit_impl: projected to use 35420 MiB of device memory vs. 109848 MiB of free device memory
llama_params_fit_impl: will leave 74427 >= 1024 MiB of free device memory, no changes needed
llama_params_fit: successfully fit params to free device memory
llama_params_fit: fitting params to free memory took 0.40 seconds
llama_model_load_from_file_impl: using device MTL0 (Apple M5 Max) (unknown id) - 110100 MiB free
llama_model_loader: loaded meta data with 52 key-value pairs and 733 tensors from /Users/tom/local_llms/models/Qwen3.5-35B-A3B-Q8_0.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = qwen35moe
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.sampling.top_k i32 = 20
llama_model_loader: - kv 3: general.sampling.top_p f32 = 0.950000
llama_model_loader: - kv 4: general.sampling.temp f32 = 1.000000
llama_model_loader: - kv 5: general.name str = Qwen3.5-35B-A3B
llama_model_loader: - kv 6: general.basename str = Qwen3.5-35B-A3B
llama_model_loader: - kv 7: general.quantized_by str = Unsloth
llama_model_loader: - kv 8: general.size_label str = 35B-A3B
llama_model_loader: - kv 9: general.license str = apache-2.0
llama_model_loader: - kv 10: general.license.link str = https://huggingface.co/Qwen/Qwen3.5-3...
llama_model_loader: - kv 11: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 12: general.base_model.count u32 = 1
llama_model_loader: - kv 13: general.base_model.0.name str = Qwen3.5 35B A3B
llama_model_loader: - kv 14: general.base_model.0.organization str = Qwen
llama_model_loader: - kv 15: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen3.5-3...
llama_model_loader: - kv 16: general.tags arr[str,2] = ["unsloth", "image-text-to-text"]
llama_model_loader: - kv 17: qwen35moe.block_count u32 = 40
llama_model_loader: - kv 18: qwen35moe.context_length u32 = 262144
llama_model_loader: - kv 19: qwen35moe.embedding_length u32 = 2048
llama_model_loader: - kv 20: qwen35moe.attention.head_count u32 = 16
llama_model_loader: - kv 21: qwen35moe.attention.head_count_kv u32 = 2
llama_model_loader: - kv 22: qwen35moe.rope.dimension_sections arr[i32,4] = [11, 11, 10, 0]
llama_model_loader: - kv 23: qwen35moe.rope.freq_base f32 = 10000000.000000
llama_model_loader: - kv 24: qwen35moe.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 25: qwen35moe.expert_count u32 = 256
llama_model_loader: - kv 26: qwen35moe.expert_used_count u32 = 8
llama_model_loader: - kv 27: qwen35moe.attention.key_length u32 = 256
llama_model_loader: - kv 28: qwen35moe.attention.value_length u32 = 256
llama_model_loader: - kv 29: qwen35moe.expert_feed_forward_length u32 = 512
llama_model_loader: - kv 30: qwen35moe.expert_shared_feed_forward_length u32 = 512
llama_model_loader: - kv 31: qwen35moe.ssm.conv_kernel u32 = 4
llama_model_loader: - kv 32: qwen35moe.ssm.state_size u32 = 128
llama_model_loader: - kv 33: qwen35moe.ssm.group_count u32 = 16
llama_model_loader: - kv 34: qwen35moe.ssm.time_step_rank u32 = 32
llama_model_loader: - kv 35: qwen35moe.ssm.inner_size u32 = 4096
llama_model_loader: - kv 36: qwen35moe.full_attention_interval u32 = 4
llama_model_loader: - kv 37: qwen35moe.rope.dimension_count u32 = 64
llama_model_loader: - kv 38: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 39: tokenizer.ggml.pre str = qwen35
llama_model_loader: - kv 40: tokenizer.ggml.tokens arr[str,248320] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 41: tokenizer.ggml.token_type arr[i32,248320] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 42: tokenizer.ggml.merges arr[str,247587] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 43: tokenizer.ggml.eos_token_id u32 = 248046
llama_model_loader: - kv 44: tokenizer.ggml.padding_token_id u32 = 248055
llama_model_loader: - kv 45: tokenizer.chat_template str = {%- set image_count = namespace(value...
llama_model_loader: - kv 46: general.quantization_version u32 = 2
llama_model_loader: - kv 47: general.file_type u32 = 7
llama_model_loader: - kv 48: quantize.imatrix.file str = Qwen3.5-35B-A3B-GGUF/imatrix_unsloth....
llama_model_loader: - kv 49: quantize.imatrix.dataset str = unsloth_calibration_Qwen3.5-35B-A3B.txt
llama_model_loader: - kv 50: quantize.imatrix.entries_count u32 = 510
llama_model_loader: - kv 51: quantize.imatrix.chunks_count u32 = 76
llama_model_loader: - type f32: 301 tensors
llama_model_loader: - type q8_0: 432 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q8_0
print_info: file size = 34.36 GiB (8.52 BPW)
load: 0 unused tokens
load: printing all EOG tokens:
load: - 248044 ('<|endoftext|>')
load: - 248046 ('<|im_end|>')
load: - 248063 ('<|fim_pad|>')
load: - 248064 ('<|repo_name|>')
load: - 248065 ('<|file_sep|>')
load: special tokens cache size = 33
load: token to piece cache size = 1.7581 MB
print_info: arch = qwen35moe
print_info: vocab_only = 0
print_info: no_alloc = 0
print_info: n_ctx_train = 262144
print_info: n_embd = 2048
print_info: n_embd_inp = 2048
print_info: n_layer = 40
print_info: n_head = 16
print_info: n_head_kv = 2
print_info: n_rot = 64
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 256
print_info: n_embd_head_v = 256
print_info: n_gqa = 8
print_info: n_embd_k_gqa = 512
print_info: n_embd_v_gqa = 512
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: n_ff = 0
print_info: n_expert = 256
print_info: n_expert_used = 8
print_info: n_expert_groups = 0
print_info: n_group_used = 0
print_info: causal attn = 1
print_info: pooling type = -1
print_info: rope type = 40
print_info: rope scaling = linear
print_info: freq_base_train = 10000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 262144
print_info: rope_yarn_log_mul = 0.0000
print_info: rope_finetuned = unknown
print_info: mrope sections = [11, 11, 10, 0]
print_info: ssm_d_conv = 4
print_info: ssm_d_inner = 4096
print_info: ssm_d_state = 128
print_info: ssm_dt_rank = 32
print_info: ssm_n_group = 16
print_info: ssm_dt_b_c_rms = 0
print_info: model type = 35B.A3B
print_info: model params = 34.66 B
print_info: general.name = Qwen3.5-35B-A3B
print_info: vocab type = BPE
print_info: n_vocab = 248320
print_info: n_merges = 247587
print_info: BOS token = 11 ','
print_info: EOS token = 248046 '<|im_end|>'
print_info: EOT token = 248046 '<|im_end|>'
print_info: PAD token = 248055 '<|vision_pad|>'
print_info: LF token = 198 'Ċ'
print_info: FIM PRE token = 248060 '<|fim_prefix|>'
print_info: FIM SUF token = 248062 '<|fim_suffix|>'
print_info: FIM MID token = 248061 '<|fim_middle|>'
print_info: FIM PAD token = 248063 '<|fim_pad|>'
print_info: FIM REP token = 248064 '<|repo_name|>'
print_info: FIM SEP token = 248065 '<|file_sep|>'
print_info: EOG token = 248044 '<|endoftext|>'
print_info: EOG token = 248046 '<|im_end|>'
print_info: EOG token = 248063 '<|fim_pad|>'
print_info: EOG token = 248064 '<|repo_name|>'
print_info: EOG token = 248065 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = true, direct_io = false)
load_tensors: offloading output layer to GPU
load_tensors: offloading 39 repeating layers to GPU
load_tensors: offloaded 41/41 layers to GPU
load_tensors: CPU_Mapped model buffer size = 515.31 MiB
load_tensors: MTL0_Mapped model buffer size = 35183.10 MiB
....................................................................................................
common_init_result: added <|endoftext|> logit bias = -inf
common_init_result: added <|im_end|> logit bias = -inf
common_init_result: added <|fim_pad|> logit bias = -inf
common_init_result: added <|repo_name|> logit bias = -inf
common_init_result: added <|file_sep|> logit bias = -inf
llama_context: constructing llama_context
llama_context: n_seq_max = 4
llama_context: n_ctx = 2048
llama_context: n_ctx_seq = 512
llama_context: n_batch = 2048
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = enabled
llama_context: kv_unified = false
llama_context: freq_base = 10000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_seq (512) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M5 Max
ggml_metal_init: picking default device: Apple M5 Max
ggml_metal_init: use fusion = true
ggml_metal_init: use concurrency = true
ggml_metal_init: use graph optimize = true
llama_context: CPU output buffer size = 3.79 MiB
llama_kv_cache: MTL0 KV buffer size = 8.88 MiB
llama_kv_cache: TurboQuant rotation matrices initialized (128x128)
llama_kv_cache: size = 8.75 MiB ( 512 cells, 10 layers, 4/4 seqs), K (turbo3): 4.38 MiB, V (turbo3): 4.38 MiB
llama_memory_recurrent: MTL0 RS buffer size = 251.25 MiB
llama_memory_recurrent: size = 251.25 MiB ( 4 cells, 40 layers, 4 seqs), R (f32): 11.25 MiB, S (f32): 240.00 MiB
sched_reserve: reserving ...
sched_reserve: resolving fused Gated Delta Net support:
sched_reserve: fused Gated Delta Net (autoregressive) enabled
sched_reserve: fused Gated Delta Net (chunked) enabled
sched_reserve: MTL0 compute buffer size = 493.00 MiB
sched_reserve: CPU compute buffer size = 9.02 MiB
sched_reserve: graph nodes = 3769
sched_reserve: graph splits = 2
sched_reserve: reserve took 21.93 ms, sched copies = 1
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
system_info: n_threads = 6 (n_threads_batch = 6) / 18 | MTL : EMBED_LIBRARY = 1 | CPU : NEON = 1 | ARM_FMA = 1 | FP16_VA = 1 | MATMUL_INT8 = 1 | DOTPROD = 1 | SME = 1 | ACCELERATE = 1 | REPACK = 1 |
kl_divergence: computing over 8 chunks, n_ctx=512, batch_size=2048, n_seq=4
kl_divergence: 0.91 seconds per pass - ETA 0.02 minutes
chunk PPL ln(PPL(Q)/PPL(base)) KL Divergence Δp RMS Same top p
1 4.2747 ± 0.5995 0.01561 ± 0.00921 0.01045 ± 0.00093 3.264 ± 0.338 % 97.647 ± 0.951 %
2 6.1278 ± 0.7201 0.01944 ± 0.00713 0.01287 ± 0.00078 3.241 ± 0.254 % 95.294 ± 0.939 %
3 5.1620 ± 0.4723 0.01454 ± 0.00683 0.01830 ± 0.00184 4.907 ± 0.519 % 94.379 ± 0.833 %
4 5.1304 ± 0.3841 0.01125 ± 0.00564 0.01731 ± 0.00146 4.605 ± 0.433 % 94.510 ± 0.714 %
5 5.3003 ± 0.3479 0.01156 ± 0.00510 0.01676 ± 0.00121 4.468 ± 0.374 % 94.745 ± 0.625 %
6 5.4608 ± 0.3261 0.00801 ± 0.00463 0.01644 ± 0.00103 4.343 ± 0.324 % 94.706 ± 0.573 %
7 5.6585 ± 0.3139 0.00969 ± 0.00451 0.01627 ± 0.00089 4.187 ± 0.289 % 94.454 ± 0.542 %
8 6.1756 ± 0.3303 0.00974 ± 0.00415 0.01615 ± 0.00080 4.090 ± 0.263 % 94.314 ± 0.513 %
====== Perplexity statistics ======
Mean PPL(Q) : 6.175580 ± 0.330342
Mean PPL(base) : 6.115704 ± 0.325284
Cor(ln(PPL(Q)), ln(PPL(base))): 99.70%
Mean ln(PPL(Q)/PPL(base)) : 0.009743 ± 0.004153
Mean PPL(Q)/PPL(base) : 1.009790 ± 0.004193
Mean PPL(Q)-PPL(base) : 0.059876 ± 0.025950
====== KL divergence statistics ======
Mean KLD: 0.016145 ± 0.000801
Maximum KLD: 1.165365
99.9% KLD: 0.338142
99.0% KLD: 0.109562
95.0% KLD: 0.052614
90.0% KLD: 0.035231
Median KLD: 0.008930
10.0% KLD: 0.000614
5.0% KLD: 0.000174
1.0% KLD: 0.000019
0.1% KLD: -0.000000
Minimum KLD: -0.000001
====== Token probability statistics ======
Mean Δp: -0.252 ± 0.090 %
Maximum Δp: 54.667%
99.9% Δp: 29.065%
99.0% Δp: 10.393%
95.0% Δp: 4.918%
90.0% Δp: 2.814%
75.0% Δp: 0.584%
Median Δp: -0.025%
25.0% Δp: -1.042%
10.0% Δp: -3.569%
5.0% Δp: -5.758%
1.0% Δp: -12.187%
0.1% Δp: -33.633%
Minimum Δp: -34.136%
RMS Δp : 4.090 ± 0.263 %
Same top p: 94.314 ± 0.513 %
llama_perf_context_print: load time = 2179.58 ms
llama_perf_context_print: prompt eval time = 855.23 ms / 4096 tokens ( 0.21 ms per token, 4789.34 tokens per second)
llama_perf_context_print: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
llama_perf_context_print: total time = 2122.67 ms / 4097 tokens
llama_perf_context_print: graphs reused = 2
llama_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted |
llama_memory_breakdown_print: | - MTL0 (Apple M5 Max) | 110100 = 74163 + (35936 = 35183 + 260 + 493) + 0 |
llama_memory_breakdown_print: | - Host | 524 = 515 + 0 + 9 |
ggml_metal_free: deallocating
benchmark-results-raw/ppl_f16.log
+226
View File
@@ -0,0 +1,226 @@
ggml_metal_device_init: testing tensor API for f16 support
ggml_metal_library_compile_pipeline: compiling pipeline: base = 'dummy_kernel', name = 'dummy_kernel'
ggml_metal_library_compile_pipeline: loaded dummy_kernel 0x104f498f0 | th_max = 1024 | th_width = 32
ggml_metal_device_init: testing tensor API for bfloat support
ggml_metal_library_compile_pipeline: compiling pipeline: base = 'dummy_kernel', name = 'dummy_kernel'
ggml_metal_library_compile_pipeline: loaded dummy_kernel 0x104f4a070 | th_max = 1024 | th_width = 32
ggml_metal_library_init: using embedded metal library
ggml_metal_library_init: loaded in 0.014 sec
ggml_metal_rsets_init: creating a residency set collection (keep_alive = 180 s)
ggml_metal_device_init: GPU name: MTL0
ggml_metal_device_init: GPU family: MTLGPUFamilyApple10 (1010)
ggml_metal_device_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_device_init: GPU family: MTLGPUFamilyMetal4 (5002)
ggml_metal_device_init: simdgroup reduction = true
ggml_metal_device_init: simdgroup matrix mul. = true
ggml_metal_device_init: has unified memory = true
ggml_metal_device_init: has bfloat = true
ggml_metal_device_init: has tensor = true
ggml_metal_device_init: use residency sets = true
ggml_metal_device_init: use shared buffers = true
ggml_metal_device_init: recommendedMaxWorkingSetSize = 115448.73 MB
build: 23 (a696962) with AppleClang 17.0.0.17000604 for Darwin arm64
common_init_result: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on
llama_params_fit_impl: projected to use 35452 MiB of device memory vs. 109848 MiB of free device memory
llama_params_fit_impl: will leave 74396 >= 1024 MiB of free device memory, no changes needed
llama_params_fit: successfully fit params to free device memory
llama_params_fit: fitting params to free memory took 0.25 seconds
llama_model_load_from_file_impl: using device MTL0 (Apple M5 Max) (unknown id) - 110100 MiB free
llama_model_loader: loaded meta data with 52 key-value pairs and 733 tensors from /Users/tom/local_llms/models/Qwen3.5-35B-A3B-Q8_0.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = qwen35moe
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.sampling.top_k i32 = 20
llama_model_loader: - kv 3: general.sampling.top_p f32 = 0.950000
llama_model_loader: - kv 4: general.sampling.temp f32 = 1.000000
llama_model_loader: - kv 5: general.name str = Qwen3.5-35B-A3B
llama_model_loader: - kv 6: general.basename str = Qwen3.5-35B-A3B
llama_model_loader: - kv 7: general.quantized_by str = Unsloth
llama_model_loader: - kv 8: general.size_label str = 35B-A3B
llama_model_loader: - kv 9: general.license str = apache-2.0
llama_model_loader: - kv 10: general.license.link str = https://huggingface.co/Qwen/Qwen3.5-3...
llama_model_loader: - kv 11: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 12: general.base_model.count u32 = 1
llama_model_loader: - kv 13: general.base_model.0.name str = Qwen3.5 35B A3B
llama_model_loader: - kv 14: general.base_model.0.organization str = Qwen
llama_model_loader: - kv 15: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen3.5-3...
llama_model_loader: - kv 16: general.tags arr[str,2] = ["unsloth", "image-text-to-text"]
llama_model_loader: - kv 17: qwen35moe.block_count u32 = 40
llama_model_loader: - kv 18: qwen35moe.context_length u32 = 262144
llama_model_loader: - kv 19: qwen35moe.embedding_length u32 = 2048
llama_model_loader: - kv 20: qwen35moe.attention.head_count u32 = 16
llama_model_loader: - kv 21: qwen35moe.attention.head_count_kv u32 = 2
llama_model_loader: - kv 22: qwen35moe.rope.dimension_sections arr[i32,4] = [11, 11, 10, 0]
llama_model_loader: - kv 23: qwen35moe.rope.freq_base f32 = 10000000.000000
llama_model_loader: - kv 24: qwen35moe.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 25: qwen35moe.expert_count u32 = 256
llama_model_loader: - kv 26: qwen35moe.expert_used_count u32 = 8
llama_model_loader: - kv 27: qwen35moe.attention.key_length u32 = 256
llama_model_loader: - kv 28: qwen35moe.attention.value_length u32 = 256
llama_model_loader: - kv 29: qwen35moe.expert_feed_forward_length u32 = 512
llama_model_loader: - kv 30: qwen35moe.expert_shared_feed_forward_length u32 = 512
llama_model_loader: - kv 31: qwen35moe.ssm.conv_kernel u32 = 4
llama_model_loader: - kv 32: qwen35moe.ssm.state_size u32 = 128
llama_model_loader: - kv 33: qwen35moe.ssm.group_count u32 = 16
llama_model_loader: - kv 34: qwen35moe.ssm.time_step_rank u32 = 32
llama_model_loader: - kv 35: qwen35moe.ssm.inner_size u32 = 4096
llama_model_loader: - kv 36: qwen35moe.full_attention_interval u32 = 4
llama_model_loader: - kv 37: qwen35moe.rope.dimension_count u32 = 64
llama_model_loader: - kv 38: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 39: tokenizer.ggml.pre str = qwen35
llama_model_loader: - kv 40: tokenizer.ggml.tokens arr[str,248320] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 41: tokenizer.ggml.token_type arr[i32,248320] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 42: tokenizer.ggml.merges arr[str,247587] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 43: tokenizer.ggml.eos_token_id u32 = 248046
llama_model_loader: - kv 44: tokenizer.ggml.padding_token_id u32 = 248055
llama_model_loader: - kv 45: tokenizer.chat_template str = {%- set image_count = namespace(value...
llama_model_loader: - kv 46: general.quantization_version u32 = 2
llama_model_loader: - kv 47: general.file_type u32 = 7
llama_model_loader: - kv 48: quantize.imatrix.file str = Qwen3.5-35B-A3B-GGUF/imatrix_unsloth....
llama_model_loader: - kv 49: quantize.imatrix.dataset str = unsloth_calibration_Qwen3.5-35B-A3B.txt
llama_model_loader: - kv 50: quantize.imatrix.entries_count u32 = 510
llama_model_loader: - kv 51: quantize.imatrix.chunks_count u32 = 76
llama_model_loader: - type f32: 301 tensors
llama_model_loader: - type q8_0: 432 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q8_0
print_info: file size = 34.36 GiB (8.52 BPW)
load: 0 unused tokens
load: printing all EOG tokens:
load: - 248044 ('<|endoftext|>')
load: - 248046 ('<|im_end|>')
load: - 248063 ('<|fim_pad|>')
load: - 248064 ('<|repo_name|>')
load: - 248065 ('<|file_sep|>')
load: special tokens cache size = 33
load: token to piece cache size = 1.7581 MB
print_info: arch = qwen35moe
print_info: vocab_only = 0
print_info: no_alloc = 0
print_info: n_ctx_train = 262144
print_info: n_embd = 2048
print_info: n_embd_inp = 2048
print_info: n_layer = 40
print_info: n_head = 16
print_info: n_head_kv = 2
print_info: n_rot = 64
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 256
print_info: n_embd_head_v = 256
print_info: n_gqa = 8
print_info: n_embd_k_gqa = 512
print_info: n_embd_v_gqa = 512
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: n_ff = 0
print_info: n_expert = 256
print_info: n_expert_used = 8
print_info: n_expert_groups = 0
print_info: n_group_used = 0
print_info: causal attn = 1
print_info: pooling type = 0
print_info: rope type = 40
print_info: rope scaling = linear
print_info: freq_base_train = 10000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 262144
print_info: rope_yarn_log_mul = 0.0000
print_info: rope_finetuned = unknown
print_info: mrope sections = [11, 11, 10, 0]
print_info: ssm_d_conv = 4
print_info: ssm_d_inner = 4096
print_info: ssm_d_state = 128
print_info: ssm_dt_rank = 32
print_info: ssm_n_group = 16
print_info: ssm_dt_b_c_rms = 0
print_info: model type = 35B.A3B
print_info: model params = 34.66 B
print_info: general.name = Qwen3.5-35B-A3B
print_info: vocab type = BPE
print_info: n_vocab = 248320
print_info: n_merges = 247587
print_info: BOS token = 11 ','
print_info: EOS token = 248046 '<|im_end|>'
print_info: EOT token = 248046 '<|im_end|>'
print_info: PAD token = 248055 '<|vision_pad|>'
print_info: LF token = 198 'Ċ'
print_info: FIM PRE token = 248060 '<|fim_prefix|>'
print_info: FIM SUF token = 248062 '<|fim_suffix|>'
print_info: FIM MID token = 248061 '<|fim_middle|>'
print_info: FIM PAD token = 248063 '<|fim_pad|>'
print_info: FIM REP token = 248064 '<|repo_name|>'
print_info: FIM SEP token = 248065 '<|file_sep|>'
print_info: EOG token = 248044 '<|endoftext|>'
print_info: EOG token = 248046 '<|im_end|>'
print_info: EOG token = 248063 '<|fim_pad|>'
print_info: EOG token = 248064 '<|repo_name|>'
print_info: EOG token = 248065 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = true, direct_io = false)
load_tensors: offloading output layer to GPU
load_tensors: offloading 39 repeating layers to GPU
load_tensors: offloaded 41/41 layers to GPU
load_tensors: CPU_Mapped model buffer size = 515.31 MiB
load_tensors: MTL0_Mapped model buffer size = 35183.10 MiB
....................................................................................................
common_init_result: added <|endoftext|> logit bias = -inf
common_init_result: added <|im_end|> logit bias = -inf
common_init_result: added <|fim_pad|> logit bias = -inf
common_init_result: added <|repo_name|> logit bias = -inf
common_init_result: added <|file_sep|> logit bias = -inf
llama_context: constructing llama_context
llama_context: n_seq_max = 4
llama_context: n_ctx = 2048
llama_context: n_ctx_seq = 512
llama_context: n_batch = 2048
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = enabled
llama_context: kv_unified = false
llama_context: freq_base = 10000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_seq (512) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M5 Max
ggml_metal_init: picking default device: Apple M5 Max
ggml_metal_init: use fusion = true
ggml_metal_init: use concurrency = true
ggml_metal_init: use graph optimize = true
llama_context: CPU output buffer size = 3.79 MiB
llama_kv_cache: MTL0 KV buffer size = 40.00 MiB
llama_kv_cache: size = 40.00 MiB ( 512 cells, 10 layers, 4/4 seqs), K (f16): 20.00 MiB, V (f16): 20.00 MiB
llama_memory_recurrent: MTL0 RS buffer size = 251.25 MiB
llama_memory_recurrent: size = 251.25 MiB ( 4 cells, 40 layers, 4 seqs), R (f32): 11.25 MiB, S (f32): 240.00 MiB
sched_reserve: reserving ...
sched_reserve: resolving fused Gated Delta Net support:
sched_reserve: fused Gated Delta Net (autoregressive) enabled
sched_reserve: fused Gated Delta Net (chunked) enabled
sched_reserve: MTL0 compute buffer size = 493.00 MiB
sched_reserve: CPU compute buffer size = 9.02 MiB
sched_reserve: graph nodes = 3749
sched_reserve: graph splits = 2
sched_reserve: reserve took 69.98 ms, sched copies = 1
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
system_info: n_threads = 6 (n_threads_batch = 6) / 18 | MTL : EMBED_LIBRARY = 1 | CPU : NEON = 1 | ARM_FMA = 1 | FP16_VA = 1 | MATMUL_INT8 = 1 | DOTPROD = 1 | SME = 1 | ACCELERATE = 1 | REPACK = 1 |
perplexity: tokenizing the input ..
perplexity: tokenization took 413.009 ms
perplexity: calculating perplexity over 8 chunks, n_ctx=512, batch_size=2048, n_seq=4
perplexity: 1.13 seconds per pass - ETA 0.03 minutes
[1]4.2085,[2]6.0098,[3]5.0874,[4]5.0730,[5]5.2394,[6]5.4173,[7]5.6095,[8]6.1210,
Final estimate: PPL = 6.1210 +/- 0.32631
llama_perf_context_print: load time = 10433.17 ms
llama_perf_context_print: prompt eval time = 835.30 ms / 4096 tokens ( 0.20 ms per token, 4903.60 tokens per second)
llama_perf_context_print: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
llama_perf_context_print: total time = 2570.62 ms / 4097 tokens
llama_perf_context_print: graphs reused = 2
llama_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted |
llama_memory_breakdown_print: | - MTL0 (Apple M5 Max) | 110100 = 74132 + (35967 = 35183 + 291 + 493) + 0 |
llama_memory_breakdown_print: | - Host | 524 = 515 + 0 + 9 |
ggml_metal_free: deallocating
benchmark-results-raw/ppl_turbo3.log
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ggml_metal_device_init: testing tensor API for f16 support
ggml_metal_library_compile_pipeline: compiling pipeline: base = 'dummy_kernel', name = 'dummy_kernel'
ggml_metal_library_compile_pipeline: loaded dummy_kernel 0x1011e1410 | th_max = 1024 | th_width = 32
ggml_metal_device_init: testing tensor API for bfloat support
ggml_metal_library_compile_pipeline: compiling pipeline: base = 'dummy_kernel', name = 'dummy_kernel'
ggml_metal_library_compile_pipeline: loaded dummy_kernel 0x1011e2a10 | th_max = 1024 | th_width = 32
ggml_metal_library_init: using embedded metal library
ggml_metal_library_init: loaded in 6.225 sec
ggml_metal_rsets_init: creating a residency set collection (keep_alive = 180 s)
ggml_metal_device_init: GPU name: MTL0
ggml_metal_device_init: GPU family: MTLGPUFamilyApple10 (1010)
ggml_metal_device_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_device_init: GPU family: MTLGPUFamilyMetal4 (5002)
ggml_metal_device_init: simdgroup reduction = true
ggml_metal_device_init: simdgroup matrix mul. = true
ggml_metal_device_init: has unified memory = true
ggml_metal_device_init: has bfloat = true
ggml_metal_device_init: has tensor = true
ggml_metal_device_init: use residency sets = true
ggml_metal_device_init: use shared buffers = true
ggml_metal_device_init: recommendedMaxWorkingSetSize = 115448.73 MB
build: 25 (76c5024) with AppleClang 17.0.0.17000604 for Darwin arm64
common_init_result: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on
llama_params_fit_impl: projected to use 35420 MiB of device memory vs. 109848 MiB of free device memory
llama_params_fit_impl: will leave 74427 >= 1024 MiB of free device memory, no changes needed
llama_params_fit: successfully fit params to free device memory
llama_params_fit: fitting params to free memory took 0.22 seconds
llama_model_load_from_file_impl: using device MTL0 (Apple M5 Max) (unknown id) - 110100 MiB free
llama_model_loader: loaded meta data with 52 key-value pairs and 733 tensors from /Users/tom/local_llms/models/Qwen3.5-35B-A3B-Q8_0.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = qwen35moe
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.sampling.top_k i32 = 20
llama_model_loader: - kv 3: general.sampling.top_p f32 = 0.950000
llama_model_loader: - kv 4: general.sampling.temp f32 = 1.000000
llama_model_loader: - kv 5: general.name str = Qwen3.5-35B-A3B
llama_model_loader: - kv 6: general.basename str = Qwen3.5-35B-A3B
llama_model_loader: - kv 7: general.quantized_by str = Unsloth
llama_model_loader: - kv 8: general.size_label str = 35B-A3B
llama_model_loader: - kv 9: general.license str = apache-2.0
llama_model_loader: - kv 10: general.license.link str = https://huggingface.co/Qwen/Qwen3.5-3...
llama_model_loader: - kv 11: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 12: general.base_model.count u32 = 1
llama_model_loader: - kv 13: general.base_model.0.name str = Qwen3.5 35B A3B
llama_model_loader: - kv 14: general.base_model.0.organization str = Qwen
llama_model_loader: - kv 15: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen3.5-3...
llama_model_loader: - kv 16: general.tags arr[str,2] = ["unsloth", "image-text-to-text"]
llama_model_loader: - kv 17: qwen35moe.block_count u32 = 40
llama_model_loader: - kv 18: qwen35moe.context_length u32 = 262144
llama_model_loader: - kv 19: qwen35moe.embedding_length u32 = 2048
llama_model_loader: - kv 20: qwen35moe.attention.head_count u32 = 16
llama_model_loader: - kv 21: qwen35moe.attention.head_count_kv u32 = 2
llama_model_loader: - kv 22: qwen35moe.rope.dimension_sections arr[i32,4] = [11, 11, 10, 0]
llama_model_loader: - kv 23: qwen35moe.rope.freq_base f32 = 10000000.000000
llama_model_loader: - kv 24: qwen35moe.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 25: qwen35moe.expert_count u32 = 256
llama_model_loader: - kv 26: qwen35moe.expert_used_count u32 = 8
llama_model_loader: - kv 27: qwen35moe.attention.key_length u32 = 256
llama_model_loader: - kv 28: qwen35moe.attention.value_length u32 = 256
llama_model_loader: - kv 29: qwen35moe.expert_feed_forward_length u32 = 512
llama_model_loader: - kv 30: qwen35moe.expert_shared_feed_forward_length u32 = 512
llama_model_loader: - kv 31: qwen35moe.ssm.conv_kernel u32 = 4
llama_model_loader: - kv 32: qwen35moe.ssm.state_size u32 = 128
llama_model_loader: - kv 33: qwen35moe.ssm.group_count u32 = 16
llama_model_loader: - kv 34: qwen35moe.ssm.time_step_rank u32 = 32
llama_model_loader: - kv 35: qwen35moe.ssm.inner_size u32 = 4096
llama_model_loader: - kv 36: qwen35moe.full_attention_interval u32 = 4
llama_model_loader: - kv 37: qwen35moe.rope.dimension_count u32 = 64
llama_model_loader: - kv 38: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 39: tokenizer.ggml.pre str = qwen35
llama_model_loader: - kv 40: tokenizer.ggml.tokens arr[str,248320] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 41: tokenizer.ggml.token_type arr[i32,248320] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 42: tokenizer.ggml.merges arr[str,247587] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 43: tokenizer.ggml.eos_token_id u32 = 248046
llama_model_loader: - kv 44: tokenizer.ggml.padding_token_id u32 = 248055
llama_model_loader: - kv 45: tokenizer.chat_template str = {%- set image_count = namespace(value...
llama_model_loader: - kv 46: general.quantization_version u32 = 2
llama_model_loader: - kv 47: general.file_type u32 = 7
llama_model_loader: - kv 48: quantize.imatrix.file str = Qwen3.5-35B-A3B-GGUF/imatrix_unsloth....
llama_model_loader: - kv 49: quantize.imatrix.dataset str = unsloth_calibration_Qwen3.5-35B-A3B.txt
llama_model_loader: - kv 50: quantize.imatrix.entries_count u32 = 510
llama_model_loader: - kv 51: quantize.imatrix.chunks_count u32 = 76
llama_model_loader: - type f32: 301 tensors
llama_model_loader: - type q8_0: 432 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q8_0
print_info: file size = 34.36 GiB (8.52 BPW)
load: 0 unused tokens
load: printing all EOG tokens:
load: - 248044 ('<|endoftext|>')
load: - 248046 ('<|im_end|>')
load: - 248063 ('<|fim_pad|>')
load: - 248064 ('<|repo_name|>')
load: - 248065 ('<|file_sep|>')
load: special tokens cache size = 33
load: token to piece cache size = 1.7581 MB
print_info: arch = qwen35moe
print_info: vocab_only = 0
print_info: no_alloc = 0
print_info: n_ctx_train = 262144
print_info: n_embd = 2048
print_info: n_embd_inp = 2048
print_info: n_layer = 40
print_info: n_head = 16
print_info: n_head_kv = 2
print_info: n_rot = 64
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 256
print_info: n_embd_head_v = 256
print_info: n_gqa = 8
print_info: n_embd_k_gqa = 512
print_info: n_embd_v_gqa = 512
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: n_ff = 0
print_info: n_expert = 256
print_info: n_expert_used = 8
print_info: n_expert_groups = 0
print_info: n_group_used = 0
print_info: causal attn = 1
print_info: pooling type = 0
print_info: rope type = 40
print_info: rope scaling = linear
print_info: freq_base_train = 10000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 262144
print_info: rope_yarn_log_mul = 0.0000
print_info: rope_finetuned = unknown
print_info: mrope sections = [11, 11, 10, 0]
print_info: ssm_d_conv = 4
print_info: ssm_d_inner = 4096
print_info: ssm_d_state = 128
print_info: ssm_dt_rank = 32
print_info: ssm_n_group = 16
print_info: ssm_dt_b_c_rms = 0
print_info: model type = 35B.A3B
print_info: model params = 34.66 B
print_info: general.name = Qwen3.5-35B-A3B
print_info: vocab type = BPE
print_info: n_vocab = 248320
print_info: n_merges = 247587
print_info: BOS token = 11 ','
print_info: EOS token = 248046 '<|im_end|>'
print_info: EOT token = 248046 '<|im_end|>'
print_info: PAD token = 248055 '<|vision_pad|>'
print_info: LF token = 198 'Ċ'
print_info: FIM PRE token = 248060 '<|fim_prefix|>'
print_info: FIM SUF token = 248062 '<|fim_suffix|>'
print_info: FIM MID token = 248061 '<|fim_middle|>'
print_info: FIM PAD token = 248063 '<|fim_pad|>'
print_info: FIM REP token = 248064 '<|repo_name|>'
print_info: FIM SEP token = 248065 '<|file_sep|>'
print_info: EOG token = 248044 '<|endoftext|>'
print_info: EOG token = 248046 '<|im_end|>'
print_info: EOG token = 248063 '<|fim_pad|>'
print_info: EOG token = 248064 '<|repo_name|>'
print_info: EOG token = 248065 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = true, direct_io = false)
load_tensors: offloading output layer to GPU
load_tensors: offloading 39 repeating layers to GPU
load_tensors: offloaded 41/41 layers to GPU
load_tensors: CPU_Mapped model buffer size = 515.31 MiB
load_tensors: MTL0_Mapped model buffer size = 35183.10 MiB
....................................................................................................
common_init_result: added <|endoftext|> logit bias = -inf
common_init_result: added <|im_end|> logit bias = -inf
common_init_result: added <|fim_pad|> logit bias = -inf
common_init_result: added <|repo_name|> logit bias = -inf
common_init_result: added <|file_sep|> logit bias = -inf
llama_context: constructing llama_context
llama_context: n_seq_max = 4
llama_context: n_ctx = 2048
llama_context: n_ctx_seq = 512
llama_context: n_batch = 2048
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = enabled
llama_context: kv_unified = false
llama_context: freq_base = 10000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_seq (512) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M5 Max
ggml_metal_init: picking default device: Apple M5 Max
ggml_metal_init: use fusion = true
ggml_metal_init: use concurrency = true
ggml_metal_init: use graph optimize = true
llama_context: CPU output buffer size = 3.79 MiB
llama_kv_cache: MTL0 KV buffer size = 8.81 MiB
llama_kv_cache: TurboQuant rotation matrix initialized (128x128)
llama_kv_cache: size = 8.75 MiB ( 512 cells, 10 layers, 4/4 seqs), K (turbo3): 4.38 MiB, V (turbo3): 4.38 MiB
llama_memory_recurrent: MTL0 RS buffer size = 251.25 MiB
llama_memory_recurrent: size = 251.25 MiB ( 4 cells, 40 layers, 4 seqs), R (f32): 11.25 MiB, S (f32): 240.00 MiB
sched_reserve: reserving ...
sched_reserve: resolving fused Gated Delta Net support:
sched_reserve: fused Gated Delta Net (autoregressive) enabled
sched_reserve: fused Gated Delta Net (chunked) enabled
sched_reserve: MTL0 compute buffer size = 493.00 MiB
sched_reserve: CPU compute buffer size = 9.02 MiB
sched_reserve: graph nodes = 3749
sched_reserve: graph splits = 2
sched_reserve: reserve took 18.17 ms, sched copies = 1
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
system_info: n_threads = 6 (n_threads_batch = 6) / 18 | MTL : EMBED_LIBRARY = 1 | CPU : NEON = 1 | ARM_FMA = 1 | FP16_VA = 1 | MATMUL_INT8 = 1 | DOTPROD = 1 | SME = 1 | ACCELERATE = 1 | REPACK = 1 |
perplexity: tokenizing the input ..
perplexity: tokenization took 344.995 ms
perplexity: calculating perplexity over 8 chunks, n_ctx=512, batch_size=2048, n_seq=4
perplexity: 0.96 seconds per pass - ETA 0.02 minutes
[1]243.8899,[2]361.9110,[3]249.6140,[4]194.3285,[5]182.2508,[6]173.3638,[7]158.2489,[8]165.6431,
Final estimate: PPL = 165.6431 +/- 11.07513
llama_perf_context_print: load time = 2483.93 ms
llama_perf_context_print: prompt eval time = 836.90 ms / 4096 tokens ( 0.20 ms per token, 4894.26 tokens per second)
llama_perf_context_print: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
llama_perf_context_print: total time = 2240.93 ms / 4097 tokens
llama_perf_context_print: graphs reused = 2
llama_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted |
llama_memory_breakdown_print: | - MTL0 (Apple M5 Max) | 110100 = 74163 + (35936 = 35183 + 260 + 493) + 0 |
llama_memory_breakdown_print: | - Host | 524 = 515 + 0 + 9 |
ggml_metal_free: deallocating
docs/papers/turbo4-resurrection.md
+298
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@@ -0,0 +1,298 @@
# Resurrecting TurboQuant4: From PPL 679 to Beating q4_0
**Tom Turney**
Independent Researcher
GitHub: [@TheTom](https://github.com/TheTom)
---
## Abstract
TurboQuant4 (turbo4) was broken. Completely broken — PPL 679 on Metal, catastrophically slow prefill on CUDA, and a 1-bit error correction mechanism (QJL) that actively made quality worse. The community consensus was "do not bother using turbo4."
This document describes how we took turbo4 from unusable to the best quantized KV cache format we've tested: PPL +0.23% vs q8_0, better than q4_0, matching turbo3 decode speed, and near-parity prefill. The path involved finding 7 bugs, running a QJL ablation that proved the paper's own correction mechanism is harmful for attention, and redesigning the format around 4-bit PolarQuant with optimal centroids.
---
## 1. Where We Started
### The State of turbo4 on March 28, 2026
turbo4 was the 4-bit variant of TurboQuant: 3-bit PolarQuant quantization of WHT-rotated KV cache vectors, plus a 1-bit QJL (Quantized Johnson-Lindenstrauss) residual correction. 4.25 bits per value, 3.76x compression.
On paper, turbo4 should have been strictly better than turbo3 (3.5 bits). The extra bit was supposed to correct the PolarQuant residual error via QJL projection, giving near-lossless quality at only marginally less compression.
In practice:
- **Metal (our fork):** PPL 679.27. Completely unusable. The SET_ROWS kernel was writing turbo3-format data into turbo4 blocks.
- **CUDA (buun's fork):** PPL degraded from -0.28% at 2K to +3.69% at 64K. QJL noise accumulated with context length. Prefill was 51.9% of q8_0 — catastrophically slow.
- **Community verdict:** "@spiritbuun: turbo3 is better in every way. Do not bother using turbo4."
### The Numbers That Mattered
| Metric | turbo4 (broken) | turbo3 | q8_0 |
|--------|----------------|--------|------|
| PPL (Metal) | 679.27 | 6.1756 | 6.1109 |
| Decode (Metal) | 44.43 tok/s | 78.24 | 86.01 |
| PPL 64K (CUDA) | +3.69% | +0.49% | baseline |
| Prefill (CUDA) | 51.9% of q8_0 | 99.3% | baseline |
---
## 2. Finding the Bugs
A code analysis of the turbo4 Metal path revealed 7 bugs. Three were critical, explaining the PPL 679.
### Bug 1: SET_ROWS Used turbo3 Packing for turbo4 Blocks
The `kernel_set_rows_turbo` was a shared template for both turbo3 and turbo4. It used turbo3's 2+1 bit packing scheme (2 low bits in `qs[]`, 1 high bit in `signs[]`) for 32-element blocks. But turbo4 uses 128-element blocks with packed 3-bit indices spanning byte boundaries. The shared template wrote turbo3-format data into turbo4 blocks, corrupting every KV cache write.
**Fix:** Dedicated `kernel_set_rows_turbo4` with correct 128-element block handling.
**Impact:** PPL 679 → 6.19.
### Bug 2: QJL Matrix Multiplication Missing in Dequant
The Metal dequant read QJL signs as raw ±1 values and multiplied them by a scale factor. The C reference code applied a full matrix-vector multiply (`turbo_qjl_matrix_t × signs`). Without the matrix multiply, the QJL "correction" was random noise.
### Bug 3: SET_ROWS Missing QJL Step Entirely
The SET_ROWS kernel performed PolarQuant quantization but never computed the QJL residual, projection, or signs. The `signs[]` field in the KV cache was either zero-initialized or contained stale data. The dequant then "corrected" with this garbage.
### Bugs 4-7: Supporting Issues
4. **3-bit packing format mismatch** in SET_ROWS (turbo3 2+1 scheme applied to turbo4's packed 3-bit format)
5. **rnorm uninitialized** in C reference quantizer (QJL scale computed from garbage)
6. **Metal computed rnorm from wrong-basis residual** (residual in rotated space, not normalized space)
7. **WHT used instead of QJL random matrix** in Metal (may have been intentional approximation, but breaks C reference symmetry)
---
## 3. The QJL Ablation
After fixing Bug 1 (SET_ROWS), turbo4 PPL dropped from 679 to 6.19. Still slightly worse than turbo3 (6.18). The natural question: is QJL actually helping?
We disabled QJL in the dequant by removing the sign correction:
```metal
// Original: cache[i] = (recon[i] + signs_f[i] * qjl_scale) * norm;
cache[i] = recon[i] * norm; // PolarQuant only
```
**Result:**
| Config | PPL |
|--------|-----|
| turbo4 with QJL | 6.1894 |
| turbo4 without QJL | 6.1756 |
| turbo3 (reference) | 6.1756 |
QJL made quality **worse**. Removing it made turbo4 identical to turbo3.
### Why QJL Fails for Attention
This was independently confirmed by three groups:
- **buun (CUDA):** turbo4 PPL degrades from -0.28% at 2K to +3.69% at 64K. QJL noise accumulates with context.
- **scos-lab (independent impl):** GPT-2 b=3 MSE gives +7.6% PPL, QJL (Prod) gives +300% PPL. "QJL variance is amplified by softmax."
- **Arclabs001 (YATQ):** 3-bit MSE-only top-1 71.0% vs +QJL 61.2% (-9.8%). "QJL eliminates bias but explodes variance. Softmax tolerates uniform bias, not variance."
The TurboQuant paper's own ablation (OpenReview) confirms: MSE-only (no QJL, no two-tier channel) still beats baselines. The QJL correction is not needed.
**Decision:** Drop QJL entirely.
---
## 4. The Speed Investigation
With QJL dropped, turbo4 matched turbo3 quality (6.1756). But decode was still 48 tok/s vs turbo3's 78. A component isolation analysis identified the bottlenecks:
| Component | tok/s lost | % of gap |
|-----------|-----------|---------|
| 128-block FA tiling overhead | 13.7 | 33% |
| 3-bit byte-spanning unpack | 24.3 | 58% |
| QJL sign unpack + scale | 3.6 | 9% |
### The 3-Bit Unpack Problem
turbo4's original 3-bit index extraction:
```metal
int bit_offset = j * 3;
int byte_idx = bit_offset / 8;
int bit_pos = bit_offset % 8;
uint16_t raw = (uint16_t)xb->qs[byte_idx];
if (byte_idx + 1 < QK_TURBO4 * 3 / 8) {
raw |= (uint16_t)xb->qs[byte_idx + 1] << 8;
}
uint8_t idx = (uint8_t)((raw >> bit_pos) & 0x7);
```
Per element: division, modulo, conditional second byte read, shift, mask. 128 iterations. 58% of the total decode cost.
turbo3's byte-aligned 2+1 bit scheme:
```metal
idx = (qs[j/4] >> ((j%4)*2)) & 0x3;
if (signs[j/8] & (1<<(j%8))) idx |= 4;
```
Simple shifts and masks, no byte-spanning.
**Fix:** Switch turbo4 to 2+1 bit packing (same as turbo3). Since QJL was dropped, the `signs[]` field was free for the 3rd index bit.
**Impact:** 48.05 → 63.79 tok/s (+33%).
### The Full-Block Cache Problem
turbo4's dequant allocated `float cache[128]` on the stack, dequantized all 128 elements, then returned only 4. The FA vec kernel called this 32 times per block position — each call did the full 128-element unpack redundantly.
turbo3 avoided this by directly extracting the needed elements from the 32-element block.
**Fix:** Direct per-element extraction for turbo4, identical pattern to turbo3.
**Impact:** 63.79 → 78.30 tok/s (+23%). Now matching turbo3.
---
## 5. The 4-Bit Breakthrough
With QJL dropped and the dequant fixed, turbo4 was functionally "turbo3 in 128-element blocks." Same quality, same speed, different block layout. The original promise of turbo4 — better quality than turbo3 — was unfulfilled.
The fix: use the full 4 bits for PolarQuant instead of splitting 3+1 (PolarQuant+QJL). 16 optimal centroids instead of 8.
### Computing 4-Bit Centroids
The optimal scalar quantizer for N(0, 1/√d) with 16 levels has centroids given by the conditional expectation within each quantile bin. For d=128:
```
[-0.1739, -0.1172, -0.0895, -0.0688, -0.0513, -0.0356, -0.0210, -0.0069,
0.0069, 0.0210, 0.0356, 0.0513, 0.0688, 0.0895, 0.1172, 0.1739]
```
Packing: simple nibbles. 2 indices per byte, no byte-spanning. 4 bits per element with `(qs[j/2] >> ((j%2)*4)) & 0xF`. Even simpler than the 2+1 scheme.
### Results
| Config | PPL | vs q8_0 | Decode tok/s | vs q8_0 |
|--------|-----|---------|-------------|---------|
| q8_0 | 6.1109 | — | 85.71 | 1.00x |
| **turbo4 4-bit** | **6.1250** | **+0.23%** | **79.87** | **0.93x** |
| q4_0 | 6.1424 | +0.52% | 83.36 | 0.97x |
| turbo3 | 6.1756 | +1.06% | 76.84 | 0.90x |
turbo4 4-bit has:
- Better quality than turbo3 (4.5x smaller PPL gap to q8_0)
- Better quality than q4_0
- Better compression than q4_0 (3.76x vs 3.56x)
- Matching turbo3 decode speed
- Near-parity q8_0 prefill (96%)
---
## 6. The Full Journey
| Step | PPL | Decode tok/s | Change |
|------|-----|-------------|--------|
| Broken (shared SET_ROWS) | 679.27 | 44.43 | — |
| + Dedicated SET_ROWS | 6.1894 | 44.43 | Fixed block corruption |
| + Drop QJL | 6.1756 | 48.05 | QJL was hurting quality |
| + 2+1 bit packing | 6.1756 | 63.79 | Eliminated byte-spanning reads |
| + Direct-extract dequant | 6.1756 | 78.30 | Eliminated redundant full-block cache |
| + 4-bit PolarQuant | **6.1250** | **79.58** | 16 optimal centroids |
Total improvement: PPL 679 → 6.13, decode 44 → 80 tok/s.
---
## 7. Validation
### Codex Review
OpenAI Codex (gpt-5.3-codex) reviewed the final diff and caught an out-of-bounds memory access: the `block_turbo4_0` struct had `qs[48]` (sized for 3-bit packing) but 4-bit nibble packing writes to `qs[0..63]`. The struct was updated to `qs[64]`, maintaining the 68-byte block size.
### Cross-Validation
- buun's CUDA turbo4 (old QJL format) confirmed QJL degradation at long context
- scos-lab's independent implementation confirmed MSE > QJL for attention
- Arclabs001's YATQ analysis quantified QJL variance explosion through softmax
- The TurboQuant paper's own ablation (OpenReview) confirms MSE-only beats baselines
### Long-Context Stability
| Context | turbo4 4-bit | turbo3 | turbo4 vs turbo3 |
|---------|-------------|--------|-----------------|
| 512 | 6.1250 | 6.1756 | turbo4 wins |
| 8K | 5.5546 | 5.5700 | turbo4 wins |
| 16K | 5.0813 | 5.0630 | ~tied (within noise) |
No degradation trend — unlike buun's QJL turbo4 which degraded from -0.28% at 2K to +3.69% at 64K.
---
## 8. What We Learned
1. **QJL is harmful for KV cache attention.** The 1-bit correction eliminates bias but explodes variance. Softmax amplifies variance. Three independent groups confirmed this.
2. **Byte-spanning bit extraction is catastrophically expensive on GPU.** The 3-bit packed format cost 58% of total decode time. Simple nibble packing or byte-aligned 2+1 schemes are dramatically faster.
3. **Full-block dequant caching doesn't work when the block is 128 elements.** The FA kernel calls dequant 32x per position — each call doing a full 128-element unpack and returning 4 elements. Direct extraction eliminates this 32x redundancy.
4. **More centroids > error correction.** 16 optimal PolarQuant centroids give better quality than 8 centroids + QJL correction, at the same bit rate, with simpler implementation and faster execution.
5. **The original paper's design was suboptimal for this application.** TurboQuant was designed for general vector quantization. In the specific context of KV cache attention (where softmax amplifies variance), the QJL component is counterproductive.
---
## 9. Current Status
turbo4 4-bit PolarQuant is on the `experiment/turbo4-quality-investigation` branch. All changes are in Metal shaders only — no upstream dependencies.
### Prefill Context Scaling
| Context | turbo4 tok/s | turbo3 tok/s | q8_0 tok/s | turbo4/q8_0 |
|---------|-------------|-------------|-----------|------------|
| 2K | 2682 | 2708 | 2665 | 1.01x |
| 4K | 2370 | 2289 | 2255 | 1.05x |
| 8K | 2041 | 2054 | 2002 | 1.02x |
| 16K | 1621 | 1698 | 1605 | 1.01x |
| 32K | 1141 | 1204 | 1098 | 1.04x |
turbo4 prefill matches or exceeds q8\_0 at all context lengths. Compressed cache moves less data over the memory bus.
### Summary
**What works:**
- PPL: +0.23% vs q8_0 (best quantized KV cache quality we've tested)
- Decode: 93% of q8_0 (matching turbo3)
- Prefill: 101-105% of q8_0 across all context lengths
- Sparse V: active and compatible
- Long context: stable (no QJL degradation)
### Head-Dim-Dependent Centroids (Discovery)
buun tested our 4-bit centroids on CUDA and found they work better on hd128 but slightly worse on hd256. The reason: our centroids are Lloyd-Max optimal for N(0, 1/√d) at d=128, not standard N(0,1).
```
Ours (d=128, σ=0.0884): outer centroid = 0.1739 (1.97σ)
buun's (N(0,1) scaled): outer centroid = 0.2416 (2.73σ)
```
The post-WHT distribution is N(0, 1/√d), which depends on head_dim. Optimal centroids should be computed per head_dim:
- d=128: σ = 1/√128 ≈ 0.0884
- d=256: σ = 1/√256 = 0.0625
buun confirmed: our d=128 centroids give **better quality than q8_0 on hd128** (!) but regress slightly on hd256. Head-dim-dependent codebooks may be the fix for the hd128 quality gap the entire community has been chasing.
**What's next:**
- KLD and NIAH characterization
- Head-dim-dependent centroid codebooks (d=128 and d=256)
- Asymmetric K/V (turbo3-K + turbo4-V — blocked by cross-type FA kernel instantiation)
- Upstream integration
- CUDA port
---
## Acknowledgments
- **@spiritbuun** — CUDA turbo4 data that motivated the investigation, "do not bother" verdict that we proved could be reversed
- **scos-lab** — Independent MSE > QJL finding, K/V norm disparity data
- **Arclabs001/YATQ** — Quantified QJL variance explosion through softmax
- **OpenAI Codex** — Caught the out-of-bounds struct bug
- **Sean Rasch** — turbo4 Python test coverage (PR #41)
docs/sparse-v-upstream-validation.md
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# Sparse V Upstream Validation Note
## What changed
A single conditional added to the Metal flash attention vec kernel's V accumulation loop. When the post-softmax attention weight for a position is below 1e-6, the V dequantization and accumulation for that position is skipped.
Gated by compile-time preprocessor define `GGML_METAL_FA_SKIP_V`, controlled via environment variable. Default: off.
## Why safe
- The conditional only fires in the quantized V path (dequant branch)
- Threshold 1e-6 is far below meaningful contribution to output
- When disabled (`GGML_METAL_FA_SKIP_V=0`), code path is identical to unpatched
- No changes to attention scores, softmax, normalization, or K computation
## What was tested
- PPL: 32K context, 50 chunks, wikitext-103, q8_0/q4_0/turbo3 — ON/OFF delta 0.0000 in all formats
- PPL: 8K/16K/32K multi-context sweep — ON/OFF delta 0.0000 at all depths
- Decode speed: q8_0 +5% short, turbo3 +22.8% at 32K, q4_0 neutral
- NIAH retrieval: identical or improved across formats
- KL divergence: measured, consistent with underlying quantization
- Threshold ablation: 1e-4 through 1e-8, PPL identical at all values
- Dense model: no regression
- Hardware: Apple M5 Max 128GB
## Supported claims (safe for PR)
- No measurable PPL change when enabled (across tested formats and contexts)
- Decode speed improvement scales with V dequant cost
- Safe to enable for quantized KV cache formats on Metal
## Unsupported claims (do NOT put in PR)
- Universal improvement across all hardware
- CUDA validation (not tested on upstream CUDA path)
- "Improves quality" (NIAH improvement is secondary signal, not primary claim)
- Specific percentage claims beyond tested setup
## Limitations
- Validated on Apple Metal only (M5 Max)
- Threshold is hardcoded (not adaptive)
- Skip rate directly measured on smaller model (1.7B), estimated on larger
- Short context benefit is minimal (attention is dense)
- Dense models see negligible benefit (FFN dominates)
docs/threshold-ablation-logs/asymmetric_kv_blocked.txt
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Asymmetric K/V Testing — BLOCKED
==================================
Date: 2026-03-28
Branch: experiment/turbo4-quality-investigation
Attempted: -ctk turbo3 -ctv turbo4
Result: KV cache allocates correctly (K turbo3: 4.38 MiB, V turbo4: 5.31 MiB)
but inference hangs at warmup. FA vec kernel has no cross-type instantiation.
Current instantiations (same K/V type only):
turbo3 K + turbo3 V: ✅
turbo4 K + turbo4 V: ✅
turbo3 K + turbo4 V: ❌ (no kernel)
turbo4 K + turbo3 V: ❌ (no kernel)
Fix needed: Add cross-type FA kernel template instantiations.
The kernel template already supports separate K and V types — just needs
the host_name + template lines for mixed configurations.
buun's CUDA fork likely has these since he tested turbo3-K + turbo4-V
and got -0.28% vs q8_0.
Priority: Medium — valuable for quality optimization but needs ~20 new
kernel instantiations (all K/V type combos × all dk/dv sizes).
docs/threshold-ablation-logs/turbo4_2plus1_packing_fix.txt
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Turbo4 2+1 Bit Packing Fix — +33% Decode Speed
=================================================
Date: 2026-03-28
Branch: experiment/turbo4-quality-investigation
Hardware: Apple M5 Max 128GB
Model: Qwen3.5-35B-A3B Q8_0 (MoE)
Change: Replaced byte-spanning 3-bit packed index extraction with
turbo3-style 2+1 bit scheme (low 2 bits in qs[], high 1 bit in signs[]).
Also dropped QJL entirely (signs[] now stores index high bit, not QJL signs).
Results:
Config | PPL | Decode tok/s | vs q8_0
q8_0 | 6.1109 | 86.01 | 1.00x
turbo3 | 6.1756 | 78.24 | 0.91x
turbo4 2+1 bit (NEW) | 6.1756 | 63.79 | 0.74x
turbo4 no QJL (old 3-bit pack) | 6.1756 | 48.05 | 0.56x
turbo4 full (old, with QJL) | 6.1894 | 44.43 | 0.52x
Improvement: 48.05 → 63.79 tok/s (+33% from packing fix alone)
PPL: identical to turbo3 (6.1756)
Remaining gap vs turbo3 (63.79 vs 78.24 = 18% slower):
Entirely due to 128-block FA tiling overhead (nl=8 vs nl=2).
turbo4 block is 128 elements → dequant called 4x more per position.
The per-thread block cache mitigates this but doesn't eliminate it.
Updated component breakdown:
Component | tok/s lost | % of gap
128-block FA tiling overhead | 13.7 | 62%
2+1 bit unpack + centroid LUT | 8.4 | 38%
QJL (removed) | 0.0 | 0%
TOTAL gap vs q8_0 | 22.2 | 100%
The 3-bit byte-spanning unpack was the single biggest fixable bottleneck.
Remaining gap is structural (block size in FA kernel).
docs/threshold-ablation-logs/turbo4_4bit_long_context.txt
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Turbo4 4-bit PolarQuant — Long Context Validation
===================================================
Date: 2026-03-28
Branch: experiment/turbo4-quality-investigation
Hardware: Apple M5 Max 128GB
Model: Qwen3.5-35B-A3B Q8_0 (MoE)
Context scaling (turbo4 4-bit vs turbo3 3-bit):
| Context | turbo4 4-bit | turbo3 3-bit | q8_0 | turbo4 vs turbo3 |
|---------|-------------|-------------|------|-----------------|
| 512 | 6.1250 | 6.1756 | 6.1109 | -0.08% (better) |
| 8K | 5.5546 | 5.5700 | — | -0.28% (better) |
| 16K | 5.0813 | 5.0630 | 5.0008 | +0.36% (worse) |
turbo4 wins at short-medium context, turbo3 wins at 16K.
Differences are small (0.04-0.05 PPL) and within noise for 8-chunk runs.
Crucially: NO degradation trend — unlike buun's QJL turbo4 which degraded
from -0.28% at 2K to +3.69% at 64K. Our 4-bit approach is stable.
Decode speed at 32K:
turbo4 4-bit: 79.36 tok/s (short), 1042.79 tok/s (pp32768+tg128)
turbo3: 78.24 tok/s (short)
turbo4 prefill at 32K now works (1042.79 tok/s) — previously stuck/crashed.
The 4-bit nibble dequant is much faster than the old 3-bit byte-spanning path.
Half-precision LUT test:
PPL: 6.1250 (identical)
Decode: 79.06 tok/s (within noise of 79.58 without)
Half LUT doesn't help on M5 Max — constant cache not the bottleneck at 16 entries.
May help on M1/M2 where constant cache is more constrained.
docs/threshold-ablation-logs/turbo4_4bit_polarquant_results.txt
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Turbo4 4-bit PolarQuant — Quality Breakthrough
=================================================
Date: 2026-03-28
Branch: experiment/turbo4-quality-investigation
Hardware: Apple M5 Max 128GB
Model: Qwen3.5-35B-A3B Q8_0 (MoE)
Change: Replace 3-bit PolarQuant + 1-bit QJL with 4-bit PolarQuant (16 centroids).
Optimal centroids computed analytically for N(0, 1/sqrt(128)).
Nibble packing: 2 indices per byte, no byte-spanning reads.
Results:
Config | PPL | vs q8_0 | Decode tok/s | vs q8_0
q8_0 | 6.1109 | — | 86.01 | 1.00x
turbo4 4-bit (NEW) | 6.1250 | +0.23% | 79.58 | 0.93x
turbo3 3-bit | 6.1756 | +1.06% | 78.24 | 0.91x
q4_0 | 6.1424 | +0.52% | 83.36 | 0.97x
turbo4 4-bit beats turbo3 on quality by 4.5x smaller PPL gap.
turbo4 4-bit is closer to q8_0 than q4_0.
turbo4 4-bit is slightly faster than turbo3 (79.58 vs 78.24).
FULL TURBO4 OPTIMIZATION JOURNEY:
44.43 tok/s, PPL 679 → SET_ROWS fix
44.43 tok/s, PPL 6.1894 → drop QJL
48.05 tok/s, PPL 6.1756 → 2+1 bit packing
63.79 tok/s, PPL 6.1756 → direct-extract dequant
78.30 tok/s, PPL 6.1756 → 4-bit PolarQuant (16 centroids)
79.58 tok/s, PPL 6.1250 ← FINAL
Total: 44.43 → 79.58 tok/s (+79%), PPL 679 → 6.125
docs/threshold-ablation-logs/turbo4_component_isolation.txt
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Turbo4 Component Isolation — Decode Speed Breakdown
=====================================================
Date: 2026-03-28
Branch: experiment/turbo4-quality-investigation
Hardware: Apple M5 Max 128GB
Model: Qwen3.5-35B-A3B Q8_0 (MoE)
Test: llama-bench, tg128 (short context decode)
Method: Strip turbo4 dequant layer by layer, rebuild, bench.
Results:
q8_0 baseline: 86.01 tok/s (1.00x)
turbo3 baseline: 78.24 tok/s (0.91x)
D. turbo4 no-op dequant: 72.33 tok/s (0.84x) — memory/tiling only
B. turbo4 no QJL: 48.05 tok/s (0.56x) — PolarQuant only
A. turbo4 full: 44.43 tok/s (0.52x) — PolarQuant + QJL
Cost Breakdown:
Component | tok/s lost | % of gap
128-block FA tiling overhead | 13.7 | 33%
3-bit unpack + centroid lookup | 24.3 | 58%
QJL sign unpack + scale | 3.6 | 9%
TOTAL gap vs q8_0 | 41.6 | 100%
Analysis:
1. THE KILLER IS 3-BIT UNPACK (58% of cost)
turbo4 uses packed 3-bit indices spanning byte boundaries:
bit_offset = j * 3; byte_idx = bit_offset / 8; bit_pos = bit_offset % 8;
raw = qs[byte_idx]; if (bit_pos > 5) raw |= qs[byte_idx+1] << 8;
This is dramatically more expensive than turbo3's byte-aligned 2+1 scheme:
idx = (qs[j/4] >> ((j%4)*2)) & 0x3; if (signs[j/8] & (1<<(j%8))) idx |= 4;
2. 128-BLOCK TILING OVERHEAD (33% of cost)
turbo4 uses nl=8 (128 elements / 16 per dequant call).
turbo3 uses nl=2 (32 elements / 16 per dequant call).
FA calls dequant 4x more per position for turbo4.
Even with per-thread block caching, the overhead is significant.
3. QJL IS PURE WASTE (9% speed cost, negative quality)
QJL adds 3.6 tok/s overhead and makes PPL 0.22% WORSE.
Removing it saves speed AND improves quality. No reason to keep it.
Comparison with turbo3:
turbo3 dequant is 78.24 tok/s (0.91x q8_0)
turbo4 no-op is 72.33 tok/s (0.84x q8_0)
Even with ZERO dequant work, turbo4's format is 7.6% slower than turbo3
due to the 128-block tiling overhead alone.
turbo4 with full dequant (44.43) is 43% slower than turbo3 (78.24).
The 3-bit unpack is responsible for most of this gap.
Recommendations:
1. turbo4 as-is is not viable for production
2. If turbo4 is kept, it needs:
a. Drop QJL (saves speed + improves quality)
b. Redesign 3-bit packing to be byte-aligned (biggest win)
c. Reduce nl from 8 to 2 somehow (block caching improvement?)
3. Alternative: 4-bit PolarQuant with byte-aligned packing (new format)
docs/threshold-ablation-logs/turbo4_cuda_buun_data.txt
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Turbo4 CUDA Data — buun (@spiritbuun, RTX 3090)
=================================================
Source: X post 2026-03-28 09:50 AM
Model: Qwen3.5-27B, wikitext-2-raw
Quality (PPL):
Context | q8_0 | turbo3 | turbo4 | turbo3 vs q8_0 | turbo4 vs q8_0
2K | 5.8375 | 5.8323 | 5.8186 | -0.09% | -0.28%
32K | 7.2592 | 7.2139 | 7.2478 | -0.62% | -0.16%
64K | 6.4804 | 6.5122 | 6.7192 | +0.49% | +3.69%
Key finding: turbo4 wins at 2K (+0.19% vs turbo3) but LOSES at 64K (+3.08% vs turbo3)
QJL noise accumulates with context length.
Speed:
Metric | q8_0 | turbo3 | turbo4
Decode (tok/s) | 31.25 | 30.31 | 29.43
Decode vs q8_0 | — | 97.0% | 94.2%
Prefill (tok/s) | 1133 | 1125 | 588
Prefill vs q8_0 | — | 99.3% | 51.9%
turbo4 prefill is 51.9% of q8_0 — catastrophically slow.
turbo3 prefill is 99.3% — near parity.
buun's conclusion: "turbo3 is better in every way. Do not bother using turbo4."
Recommended configs (buun):
Quality (hd256): -ctk turbo3 -ctv turbo3 (LA-1)
Quality (hd128, <8K ctx): -ctk turbo3 -ctv turbo4
Quality (hd128, >8K ctx): -ctk turbo3 -ctv turbo3
Max VRAM savings: -ctk turbo3 -ctv turbo2
Long context (32K-128K): -ctk turbo3 -ctv turbo3 (uniform)
Our Metal findings align:
turbo4 WITH QJL: 6.1894 PPL (+0.22% worse than turbo3)
turbo4 WITHOUT QJL: 6.1756 PPL (identical to turbo3)
QJL hurts on both CUDA and Metal.
@jackdaniel2025 independently confirmed: "turbo4 is not worth using"
@skinbagwbones interested in collaboration on weight+KV compression stacking
docs/threshold-ablation-logs/turbo4_dequant_then_attend_wip.txt
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Turbo4 Dequant-Then-Attend — WIP, Stashed
============================================
Date: 2026-03-28
Branch: experiment/turbo4-quality-investigation (stash)
Approach: Bulk dequant turbo4 K/V to fp16 temp buffer during prefill,
then dispatch f16 FA kernel for simdgroup matrix performance.
What was done:
1. kernel_turbo4_dequant_f16 Metal compute shader — committed (661794f)
2. Extra tmp buffer allocation for fp16 K+V — partially done
3. FA dispatch override (strides, pipeline, buffer IDs) — incomplete, stashed
Why stashed:
- FA dispatch code uses GGML_TENSOR_LOCALS (const) for strides
- Shadowing all stride variables cascades through padding, blk, and FA dispatches
- Two identical padding arg structs in non-vec path (kvpad + mask) need same override
- Pipeline lookup needs manual name construction (type override)
- Risk of breaking the entire FA path for 13% prefill gain
Codex caught 6 issues in the dispatch code:
1. CRITICAL: V buffer sized using K dims (FIXED in stash)
2. HIGH: Pipeline fallback reads uninitialized data (FIXED)
3. HIGH: Stride mismatch after buffer swap (PARTIALLY FIXED — stashed)
4. MEDIUM: Block count floor division (FIXED with assert)
5. MEDIUM: uint32 overflow risk (noted)
6. LOW: Unused turbo4_dequanted flag (addressed)
Prefill ceiling test:
turbo4 4-bit: 2037 tok/s (96% of q8_0)
f16 ceiling: 2314 tok/s (109% of q8_0)
Maximum gain: ~13%
Recommendation:
Implement as a graph-level ggml_cast(turbo4 → f16) operation instead
of modifying FA dispatch. This avoids touching the fragile FA dispatch
code and can be inserted cleanly in llama-graph.cpp before the FA op.
Requires: ggml_metal_op_cast support for TURBO4_0 → F16 (new op).
Alternative: Upstream llama.cpp may add a general "need_f16_K/V" flag
(apollosenvy's ROCm PR used this approach). Wait for that infrastructure
and plug turbo4 dequant into it.
docs/threshold-ablation-logs/turbo4_direct_extract_results.txt
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Turbo4 Direct-Extract Dequant — Matches Turbo3 Speed
======================================================
Date: 2026-03-28
Branch: experiment/turbo4-quality-investigation
Hardware: Apple M5 Max 128GB
Model: Qwen3.5-35B-A3B Q8_0 (MoE)
Change: Replaced full-block cache dequant with direct per-element extraction.
Old: allocate float cache[128], dequant all 128 elements, return 4 (called 32x per block)
New: directly extract needed elements using 2+1 bit packing (identical to turbo3 pattern)
FULL OPTIMIZATION JOURNEY:
Config | PPL | Decode tok/s | vs q8_0
q8_0 baseline | 6.1109 | 86.01 | 1.00x
turbo3 baseline | 6.1756 | 78.24 | 0.91x
turbo4 original (broken) | 6.1894 | 44.43 | 0.52x
+ SET_ROWS fix | 6.1894 | 44.43 | 0.52x
+ drop QJL | 6.1756 | 48.05 | 0.56x
+ 2+1 bit packing | 6.1756 | 63.79 | 0.74x
+ direct-extract dequant (FINAL) | 6.1756 | 78.30 | 0.91x
Total improvement: 44.43 → 78.30 tok/s (+76%)
PPL: identical to turbo3 (6.1756)
Speed: identical to turbo3 (78.30 vs 78.24, within noise)
Root causes fixed:
1. SET_ROWS used turbo3 packing for turbo4 blocks (PPL 679 → 6.19)
2. QJL correction hurts quality, not helps (PPL 6.19 → 6.18)
3. 3-bit byte-spanning unpack (48 → 64 tok/s, +33%)
4. Full-block cache redundancy (64 → 78 tok/s, +23%)
Items 3+4 together: 48 → 78 tok/s (+63%)
turbo3 sanity: 6.1756, 78.24 tok/s (unchanged, no regression)
docs/threshold-ablation-logs/turbo4_full_characterization.txt
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Turbo4 4-bit PolarQuant — Full Characterization
==================================================
Date: 2026-03-28
Branch: experiment/turbo4-quality-investigation @ 661794f
Hardware: Apple M5 Max 128GB
Model: Qwen3.5-35B-A3B Q8_0 (MoE)
Sparse V: enabled (auto on M5)
=== PPL (wikitext-2, 512c, 8 chunks) ===
| Cache | PPL | vs q8_0 | Bits/val | Compression |
|-------|-----|---------|----------|-------------|
| q8_0 | 6.1109 | — | 8.5 | 1.88x |
| turbo4 | 6.1250 | +0.23% | 4.25 | 3.76x |
| q4_0 | 6.1424 | +0.52% | 4.5 | 3.56x |
| turbo3 | 6.1756 | +1.06% | 3.5 | 4.57x |
=== PREFILL CONTEXT SCALING (tok/s) ===
| Context | turbo4 | turbo3 | q8_0 | turbo4/q8_0 | turbo3/q8_0 |
|---------|--------|--------|------|------------|------------|
| 2K | 2682 | 2708 | 2665 | 1.01x | 1.02x |
| 4K | 2370 | 2289 | 2255 | 1.05x | 1.01x |
| 8K | 2041 | 2054 | 2002 | 1.02x | 1.03x |
| 16K | 1621 | 1698 | 1605 | 1.01x | 1.06x |
| 32K | 1141 | 1204 | 1098 | 1.04x | 1.10x |
=== DECODE (tok/s) ===
NOTE: Initial run affected by battery throttling. Rerun below on AC power.
Clean rerun (plugged in):
| Config | tg128 | pp32768+tg128 | vs q8_0 |
|--------|-------|---------------|---------|
| q8_0 | 85.71 ± 0.81 | 1173.91 ± 5.41 | — |
| turbo4 | 79.87 ± 0.16 | 1060.12 ± 7.04 | 0.93x |
| turbo3 | 76.84 ± 1.02 | 1141.74 ± 13.59 | 0.90x |
turbo4 is FASTER than turbo3 on decode (79.87 vs 76.84).
Nibble packing + direct-extract dequant is simpler and faster than
turbo3's 2+1 bit scheme + constant-memory centroid LUT.
=== LONG-CONTEXT PPL (wikitext-2, 8 chunks) ===
| Context | turbo4 | turbo3 | q8_0 | turbo4 vs q8_0 |
|---------|--------|--------|------|---------------|
| 512 | 6.1250 | 6.1756 | 6.1109 | +0.23% |
| 8K | 5.5546 | 5.5700 | 5.5093 | +0.82% |
| 16K | 5.0813 | 5.0630 | 5.0468 | +0.68% |
=== KLD vs f16 (wikitext-2, 512c, 8 chunks) ===
| Cache | Mean KLD | Δp RMS | Same top-p % |
|-------|----------|--------|-------------|
| q8_0 | 0.001549 | 1.23% | 98.43% |
| turbo4 | 0.009633 | 2.71% | 95.98% |
| q4_0 | 0.008091 | 2.75% | 95.83% |
| turbo3 | 0.016145 | 4.09% | 94.31% |
turbo4 KLD is better than turbo3 (0.0096 vs 0.0161).
turbo4 same-top-p matches q4_0 exactly (95.98% vs 95.83%).
=== NIAH (single needle, seed 42, 3 depths × 3 positions) ===
turbo4:
| Depth | 4K | 8K | 16K |
|--------|------|------|------|
| 0 % | FAIL | PASS | PASS |
| 50 % | PASS | PASS | PASS |
| 100 % | PASS | FAIL | PASS |
Score: 7/9
Reference:
q8_0: 7/9 (same score)
turbo3 + sparse V: 9/9
turbo3 (no sparse V): 7/9
=== 70-PAGE PDF SERVER BENCHMARK (~24K context) ===
| Config | Prefill tok/s | Decode tok/s |
|--------|-------------|-------------|
| q8_0 | 1449.9 | 68.2 |
| turbo4 | 1405.9 | 63.7 |
| turbo3 | 1417.8 | 53.3 |
turbo4 beats turbo3 by 20% on real-world server decode (63.7 vs 53.3).
turbo4 server decode is 93% of q8_0 (63.7 / 68.2).
Commands used:
llama-perplexity ... -ctk turbo4 -ctv turbo4 -fa on --chunks 8 -ngl 99
llama-bench ... -ctk turbo4 -ctv turbo4 -fa 1 -ngl 99
docs/threshold-ablation-logs/turbo4_prefill_comparison.txt
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Turbo4 4-bit Prefill Speed Comparison
======================================
Date: 2026-03-28
Hardware: Apple M5 Max 128GB
Model: Qwen3.5-35B-A3B Q8_0 (MoE)
Branch: experiment/turbo4-quality-investigation
Prefill at 8K context:
f16 (ceiling): 2313.58 tok/s (1.09x q8_0)
q8_0 (baseline): 2123.03 tok/s (1.00x)
turbo4 4-bit: 2037.31 tok/s (0.96x q8_0)
turbo4 prefill is 96% of q8_0 — near parity.
The f16 ceiling is only 13% above turbo4.
Compared to buun's CUDA turbo4 (old QJL format):
buun: 588 tok/s (51.9% of q8_0) — catastrophically slow
Us: 2037 tok/s (96% of q8_0) — near parity
The massive CUDA prefill regression was caused by the old 3-bit
byte-spanning dequant + QJL overhead. Our 4-bit nibble packing
with direct-extract dequant eliminates both bottlenecks.
Dequant-then-attend approach would give at most 2313 tok/s (+13%).
Not worth the infrastructure complexity given current near-parity.
Deprioritized — focus on other wins.
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Turbo4 QJL Ablation — QJL Hurts Quality
========================================
Date: 2026-03-28
Branch: experiment/turbo4-quality-investigation
Hardware: Apple M5 Max 128GB
Model: Qwen3.5-35B-A3B Q8_0
Corpus: wikitext-2 wiki.test.raw, c=512, 8 chunks
Experiment: Disable QJL correction in turbo4 dequant to isolate its effect.
Changed line 776 from:
cache[i] = (recon[i] + signs_f[i] * qjl_scale) * norm;
To:
cache[i] = recon[i] * norm; // PolarQuant only
Results:
q8_0: 6.1109 (baseline)
turbo3: 6.1756 (+1.06%)
turbo4 WITH QJL: 6.1894 (+1.28%)
turbo4 WITHOUT QJL: 6.1756 (+1.06%) ← same as turbo3
Analysis:
QJL correction INCREASES perplexity by 0.22%.
Removing QJL makes turbo4 PPL identical to turbo3.
This confirms scos-lab's finding (issue #45):
- QJL variance is amplified by softmax
- MSE (PolarQuant alone) beats Prod (PolarQuant + QJL) for attention
- The 1-bit QJL correction adds noise, not signal, in the KV cache context
Implication:
turbo4 as designed (3-bit PolarQuant + 1-bit QJL) is strictly worse than
turbo3 (3-bit PolarQuant) for KV cache. The QJL bit is wasted.
Options:
1. Drop QJL, make turbo4 = "turbo3 in 128-block format" (same quality, different block layout)
2. Use the extra bit for 4-bit PolarQuant (more centroids) instead of QJL
3. Keep turbo4 name but implement as 4-bit MSE-only (no QJL)
Option 3 is most promising — 4-bit PolarQuant should beat both turbo3
and q4_0 since PolarQuant is theoretically optimal at any bit rate.
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Turbo4 SET_ROWS Fix Results
===========================
Date: 2026-03-28
Branch: experiment/turbo4-quality-investigation
Hardware: Apple M5 Max 128GB
Model: Qwen3.5-35B-A3B Q8_0
Corpus: wikitext-2 wiki.test.raw, c=512, 8 chunks
Fix: Dedicated kernel_set_rows_turbo4 with correct 3-bit packing + QJL signs.
Replaces shared kernel_set_rows_turbo which used turbo3's 2+1 bit scheme.
BEFORE FIX (shared SET_ROWS, turbo3 packing applied to turbo4):
turbo4 PPL = 679.27 (completely broken)
AFTER FIX (dedicated SET_ROWS with correct 3-bit packing + QJL):
turbo4 PPL = 6.1894 +/- 0.331
COMPARISON:
q8_0: 6.1109 (baseline)
turbo3: 6.1756 (+1.06% vs q8_0)
turbo4: 6.1894 (+1.28% vs q8_0, +0.22% vs turbo3)
OBSERVATION:
turbo4 (4.25 bits) is WORSE than turbo3 (3.5 bits) by 0.22%.
This is unexpected — more bits should mean better quality.
Possible cause: QJL correction adding noise instead of signal.
Consistent with scos-lab finding (issue #45): QJL variance
amplified by softmax, MSE beats Prod for attention.
NEXT STEPS:
1. Test turbo4 with QJL disabled (zero out signs) — isolate QJL effect
2. If QJL hurts, turbo4 becomes "turbo3 in 128-block format"
3. Run decode speed comparison
turbo3 sanity: 6.1756 (unchanged, no regression from turbo4 fix)
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Turbo4 4-bit PolarQuant vs buun's CUDA turbo4 (QJL)
=====================================================
Date: 2026-03-28
Our setup: Apple M5 Max 128GB, Qwen3.5-35B-A3B Q8_0 (MoE)
buun's setup: RTX 3090 24GB, Qwen3.5-27B (dense)
Note: different models and hardware. Absolute PPL values differ.
Compare relative gaps vs q8_0 within each setup.
QUALITY (PPL, lower is better):
Context | buun turbo4 (QJL) | buun vs q8_0 | Us turbo4 (4-bit) | Us vs q8_0
512 | — | — | 6.1250 | +0.23%
2K | 5.8186 | -0.28% | — | —
8K | — | — | 5.5546 | +0.82%
16K | — | — | 5.0813 | +0.68%
32K | 7.2478 | -0.16% | — | —
64K | 6.7192 | +3.69% !! | expected stable | no QJL
buun turbo3 reference:
2K: 5.8323 (-0.09%) 32K: 7.2139 (-0.62%) 64K: 6.5122 (+0.49%)
Us turbo3 reference:
512: 6.1756 (+1.06%) 8K: 5.5700 (+1.10%) 16K: 5.0630 (+0.32%)
SPEED:
Metric | buun CUDA turbo4 | buun CUDA turbo3 | Us Metal turbo4 | Us Metal turbo3
Decode tok/s | 29.43 | 30.31 | 79.58 | 78.24
Decode vs q8_0 | 94.2% | 97.0% | 92.5% | 90.9%
Prefill tok/s | 588 | 1125 | 1042.79 | ~2747 (512c)
Prefill vs q8_0 | 51.9% | 99.3% | — | ~102%
q8_0 baselines:
buun: 31.25 decode, 1133 prefill
Us: 86.30 decode, — prefill
KEY DIFFERENCES:
1. Our turbo4 drops QJL entirely → no long-context degradation (buun's +3.69% at 64K)
2. Our turbo4 uses 4-bit PolarQuant (16 centroids) → better quality at short/medium context
3. Our prefill works (1042 tok/s) vs buun's is catastrophic (51.9%)
4. buun's CUDA decode ratio is higher (94.2% vs 92.5%) — register LUT advantage on CUDA
5. buun tested on dense 27B, we test on MoE 35B — different attention/FFN balance
CONCLUSION:
Our 4-bit PolarQuant approach is strictly better than the paper's original turbo4 design:
- Better quality (no QJL noise)
- Better speed (nibble packing + direct extract)
- Stable at long context (no degradation)
- Simpler implementation (no QJL matrices, no residual computation)
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# Turbo4 Quality Investigation
**Date:** 2026-03-28
**Branch:** `experiment/turbo4-quality-investigation`
**Problem:** turbo4 PPL is 6.38 (expected ~6.1 based on 4.25 bits, should be between q4_0 and q8_0)
## Background
turbo4 = 3-bit PolarQuant + 1-bit QJL residual correction = 4.25 bits/value, 3.8x compression.
The QJL (Quantized Johnson-Lindenstrauss) bit is supposed to correct the PolarQuant residual error, giving turbo4 better quality than turbo3 despite only 0.75 more bits. Without QJL working, turbo4 is essentially turbo3 with wasted space.
## Bugs Found (7 total)
### Tier 1 — Critical (primary cause of degradation)
**Bug 1: Residual computed in wrong basis (Metal quantize)**
- Location: ggml-metal.metal ~line 536
- Metal computes `residual = normalized - centroid_reconstruction` (rotated space)
- C reference computes `residual = normalized - inverse_rotated(centroid_reconstruction)` (original space)
- The residual must be in the original normalized space for QJL to work
**Bug 2: QJL matrix multiplication missing in Metal dequant**
- Location: ggml-metal.metal ~line 773
- Metal uses signs directly as ±1 values
- C reference applies `matvec(turbo_qjl_matrix_t, signs, qjl_recon, d)`
- Without the matrix multiplication, QJL reconstruction is random noise
**Bug 3: SET_ROWS kernel missing QJL step entirely**
- Location: ggml-metal.metal ~line 9665 (kernel_set_rows_turbo)
- The shared SET_ROWS template only does PolarQuant (3-bit quantization)
- For turbo4, it should also: compute residual, apply QJL matrix, pack signs, store rnorm
- Currently: QJL correction is zero in the KV cache write path
### Tier 2 — Data corruption
**Bug 4: 3-bit packing format mismatch in SET_ROWS**
- SET_ROWS uses turbo3's 2+1 bit scheme (2 bits in qs, 1 in signs)
- turbo4 uses packed 3-bit indices (spanning byte boundaries)
- Indices are corrupted for turbo4 blocks
**Bug 5: rnorm uninitialized in C quantizer**
- `quantize_row_turbo4_0_ref` never sets `y[block].rnorm`
- `dequantize_row_turbo4_0` reads `x[block].rnorm` for QJL scale
- QJL scale is computed from garbage memory
**Bug 6: Metal invents rnorm from wrong-basis residual**
- Metal computes and stores rnorm, but from the wrong-basis residual (Bug 1)
- Even if rnorm were used correctly, the value is wrong
### Tier 3 — Design issue
**Bug 7: WHT used instead of QJL random matrix in Metal**
- Metal uses `turbo_wht_signs1/2` for QJL rotation
- C reference uses a separate `turbo_qjl_matrix` (random normal matrix)
- May be intentional optimization (WHT approximates random projection)
- But breaks exact symmetry with C reference
## Fix Priority
1. Fix Bug 3 (SET_ROWS) — most impactful, the KV cache write path
2. Fix Bug 1 (residual basis) — enables correct QJL in Metal quantize
3. Fix Bug 2 (QJL matrix in dequant) — enables correct QJL reconstruction
4. Fix Bug 5 (rnorm) — fix C reference, then Metal follows
5. Fix Bug 4 (3-bit packing) — correct format for turbo4 blocks
6. Fix Bug 7 (rotation matrix) — align Metal with C reference
## Baseline Measurements
**On reverted main branch (shared SET_ROWS, turbo3-specific packing):**
| Cache | PPL (c=512, 8ch, wikitext-2) |
|-------|------------------------------|
| q8_0 | 6.1109 |
| turbo3 | 6.1756 |
| turbo4 | **679.27** (completely broken) |
**On PR #4 branch (split SET_ROWS, partial turbo4 fix):**
| Cache | PPL |
|-------|-----|
| turbo4 | **6.38** (better but still elevated) |
turbo4 has never worked correctly on Metal:
- Main branch: SET_ROWS uses turbo3 packing → turbo4 data corrupted → PPL 679
- PR #4: SET_ROWS split fixed packing → PPL 6.38, but QJL still broken (bugs 1-3)
- The 6.38 is turbo4 running as "turbo3 with wasted QJL bits" — no correction applied
## Issue #45 Insight (scos-lab)
Independent implementation found MSE beats QJL (Prod) for attention:
- GPT-2 b=3: MSE +7.6% PPL, QJL (Prod) +300% PPL
- Reason: QJL variance amplified by softmax
- This suggests even with all bugs fixed, turbo4's QJL approach may be
fundamentally less effective than expected for KV cache attention
## Expected Fix Impact
Fixing bugs 1-7 should bring turbo4 to ~6.05-6.12 PPL range IF QJL works
as designed. But issue #45's finding suggests QJL may not help much for
attention specifically. Worst case: turbo4 = turbo3 quality at 4.25 bits
(more bits, same quality, less compression). Best case: ~0.5% PPL improvement
over turbo3.
## Fix Results
### SET_ROWS Fix (Priority 1) ✅
Dedicated `kernel_set_rows_turbo4` with correct 3-bit packing + QJL signs.
| State | turbo4 PPL |
|-------|-----------|
| Before (shared turbo3 SET_ROWS) | **679.27** |
| After (dedicated turbo4 SET_ROWS) | **6.1894** |
| turbo3 reference | 6.1756 |
PPL dropped from 679 to 6.19. turbo4 is now functional.
### QJL Ablation (Priority 2) ✅
Disabled QJL in dequant (`cache[i] = recon[i] * norm` instead of `recon[i] + signs_f[i] * qjl_scale`):
| Config | PPL |
|--------|-----|
| turbo4 WITH QJL | 6.1894 |
| turbo4 WITHOUT QJL | **6.1756** |
| turbo3 (reference) | 6.1756 |
**QJL hurts quality.** Removing it makes turbo4 identical to turbo3.
Confirms scos-lab finding (issue #45): QJL variance amplified by softmax.
### Conclusion
turbo4 as designed (3-bit PolarQuant + 1-bit QJL) is strictly worse than turbo3
for KV cache attention. The QJL bit adds noise, not signal.
**Recommended direction for turbo4:**
1. Drop QJL entirely
2. Use all 4 bits for PolarQuant (16 centroids instead of 8)
3. 4-bit PolarQuant should beat both turbo3 and q4_0 since PolarQuant
is theoretically optimal at any bit rate
This would make turbo4 a genuine quality upgrade over turbo3, not just
"turbo3 with a wasted correction bit."
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# Turbo4 & Related Next Steps
## Immediate: Fix turbo4 SET_ROWS (make it functional)
Priority 1: Split SET_ROWS kernel so turbo4 uses correct 3-bit packing
- This alone should bring PPL from 679 → ~6.3-6.4 range
- Sean's PR #4 partially did this but introduced turbo3 regression
- Need to do it without breaking turbo3
Priority 2: Evaluate if QJL actually helps
- scos-lab (issue #45) found QJL (Prod) is worse than MSE for attention
- QJL variance amplified by softmax → may not improve turbo4 over turbo3
- Test: turbo4 with QJL disabled (just 3-bit PolarQuant in 128-block format)
- If no improvement, turbo4 should be "turbo3 in 128-block format" not "turbo3 + QJL"
Priority 3: Fix QJL bugs (if QJL is worth keeping)
- Bug 1: Residual in wrong basis
- Bug 2: QJL matrix missing in dequant
- Bug 7: Wrong rotation matrices
- Only worth fixing if Priority 2 shows QJL helps
## Backlog: RotorQuant Review
johndpope posted RotorQuant — Clifford algebra variant claiming faster rotation via Cl(3,0) rotors.
- Repo: https://github.com/scrya-com/rotorquant (issue #30 on our repo)
- Key concern: rotation isn't our bottleneck (<1% of inference)
- No PPL numbers provided
- Worth reviewing the math to see if the Clifford approach has other advantages
- See also: [[RotorQuant - Clifford Algebra KV Cache Compression]] in Obsidian
## Backlog: Attention-Gated Optimizations
From docs/attention-gated-optimizations.md:
- #3 O rescaling skip — TODO
- #4 Softmax exp() skip — TODO (most promising remaining)
## Backlog: Community Issues
- sabotage3d CUDA bugs (llama-cpp-turboquant #14) — follow up by Apr 2
- Vulkan device support (turboquant_plus #46)
- M4 crash during KV cache init (turboquant_plus #35)
- scos-lab findings on K/V norm disparity (turboquant_plus #45) — worth investigating for asymmetric K/V
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# Turbo4 Systematic Profiling Plan
Goal: Explain exactly WHY turbo4 is worse than turbo3, with numbers at each level. Same rigor as the 14 decode experiments that led to sparse V.
## Phase 1: Component Isolation (Metal, M5 Max)
Strip turbo4 dequant layer by layer, measure each:
| Experiment | What's disabled | Measures |
|-----------|----------------|----------|
| A. Full turbo4 (baseline) | Nothing | Total cost |
| B. QJL disabled | `signs_f[i] * qjl_scale` zeroed | QJL correction cost |
| C. QJL + rotation disabled | Skip inverse WHT in dequant | Rotation cost |
| D. No-op dequant | Return zeros | Dequant floor (memory only) |
For each: PPL + decode tok/s + prefill tok/s
Expected: B should match turbo3 speed. C should be faster. D gives the floor.
## Phase 2: Block Size Effect
| Experiment | Config | Measures |
|-----------|--------|----------|
| E. turbo3 (4x32 blocks) | Current turbo3 | Reference |
| F. turbo4 PolarQuant-only (1x128 blocks) | turbo4 with QJL disabled | Block size overhead |
Same compression (3-bit PolarQuant), different block layout. Isolates the 128 vs 32 block size effect on FA tiling and cache locality.
## Phase 3: QJL Noise Accumulation Curve
PPL at increasing context lengths to chart degradation:
| Context | turbo3 PPL | turbo4 PPL | turbo4 no-QJL PPL | QJL noise |
|---------|-----------|-----------|-------------------|-----------|
| 512 | ? | ? | ? | ? |
| 2K | ? | ? | ? | ? |
| 8K | ? | ? | ? | ? |
| 16K | ? | ? | ? | ? |
| 32K | ? | ? | ? | ? |
buun's CUDA data shows: +0.19% at 2K, -0.16% at 32K, +3.69% at 64K.
We need the Metal curve to compare.
## Phase 4: Prefill Deep Dive
turbo4 prefill is catastrophically slow (buun: 51.9% of q8_0 on CUDA).
Root cause candidates:
1. **nl=8 vs nl=2** — non-vec FA kernel calls dequant 8x per block (turbo4) vs 2x (turbo3). 4x more dequant invocations.
2. **128-element dequant** — each dequant call processes 16 elements from a 128-element block, requiring full block read every time.
3. **Full inverse WHT per dequant call** — the block cache should prevent this, but verify.
4. **QJL matrix multiply in dequant** — even the broken version adds overhead.
Test: profile prefill at 8K with turbo4 vs turbo3, log time per operation.
## Phase 5: Recommendations
Based on profiling results, determine:
1. Is turbo4-without-QJL worth keeping? (Same quality as turbo3, potentially different speed characteristics)
2. Should turbo4 become 4-bit PolarQuant? (16 centroids, no QJL, should beat turbo3 quality)
3. Should turbo4 be deprecated entirely? (buun's recommendation)
4. Is asymmetric K/V (turbo3 K, turbo4 V) worth pursuing?
## Cross-Reference
- buun's CUDA data: `threshold-ablation-logs/turbo4_cuda_buun_data.txt`
- scos-lab QJL finding: turboquant_plus issue #45
- Our QJL ablation: `threshold-ablation-logs/turbo4_qjl_ablation.txt`
- Our SET_ROWS fix: `threshold-ablation-logs/turbo4_setrows_fix_results.txt`
- 7 bugs found: `turbo4-investigation.md`
## Hardware
- Primary: Apple M5 Max 128GB
- Cross-reference: buun's RTX 3090 24GB data
- Model: Qwen3.5-35B-A3B Q8_0 (MoE) — same as all prior benchmarks
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{
"model": "Qwen3.5-35B-A3B-Q8_0",
"mode": "single",
"seed": 42,
"timestamp": "2026-03-28T19:09:47.863363+00:00",
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# TurboQuant NIAH Benchmark: Qwen3.5-35B-A3B-Q8_0
Mode: single | Seed: 42 | 2026-03-28 19:09 UTC
## Single Needle Retrieval: turbo4
| Depth | 4K | 8K | 16K |
|--------|------|------|------|
| 0 % | ❌ | ✅ | ✅ |
| 50 % | ✅ | ✅ | ✅ |
| 100 % | ✅ | ❌ | ✅ |