docs: sparse V validated on q8_0 — +5% decode, confirms generality

q8_0 with sparse V: 80.7 → 84.7 tok/s (+5.0%) on M5 Max.
Not TurboQuant-specific — any quantized KV cache benefits.
Added TURBO_SPARSE_V=0 disable override for A/B testing.
Raw logs included.

Co-Authored-By: tturney@psyguard.ai
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

docs/papers/sparse-v-dequant.md

@@ -221,6 +221,19 @@ Sparse V dequantization is not specific to TurboQuant. It applies to any quantiz
 
 This includes NVFP4, KIVI, CacheQuant, and other KV cache quantization methods. The 3-line implementation is kernel-level and requires no model changes, no retraining, and no calibration data.
 
+### 7.1 Empirical Validation on q8\_0
+
+To validate generality beyond TurboQuant, we tested sparse V on llama.cpp's standard q8\_0 KV cache (8-bit quantization, 2× compression) using the same model and hardware. A `TURBO_SPARSE_V=0` override was added to force-disable the optimization for A/B comparison:
+
+| Test | q8\_0 + sparse V | q8\_0 (no sparse V) | Improvement |
+|------|-----------------|---------------------|-------------|
+| Decode (short, tg128) | 84.7 tok/s | 80.7 tok/s | **+5.0%** |
+| Blended (pp32768+tg128) | 1145.2 tok/s | 1096.7 tok/s | **+4.4%** |
+
+Sparse V provides a **5% decode speedup on q8\_0** — a cache format with far cheaper per-position dequantization than turbo3. The benefit is smaller than turbo3's +22.8% at 32K because q8\_0's dequant is lightweight (simple scale-and-add vs centroid LUT + WHT rotation), but the attention sparsity still allows meaningful work to be skipped.
+
+This confirms that sparse V is a general flash attention optimization, not a TurboQuant-specific trick. Any quantized cache format — including NVFP4, q4\_0, and future formats — should benefit. Raw benchmark logs: [`threshold-ablation-logs/q8_0_sparse_v_ablation_m5.txt`](../threshold-ablation-logs/q8_0_sparse_v_ablation_m5.txt).
+
 ---
 
 ## 8. Limitations and Future Work

docs/threshold-ablation-logs/q8_0_sparse_v_ablation_m5.txt

@@ -0,0 +1,20 @@
+Sparse V impact on q8_0 (non-TurboQuant) KV cache
+Hardware: Apple M5 Max 128GB
+Model: Qwen3.5-35B-A3B Q8_0
+Date: 2026-03-27
+
+=== Q8_0 WITH SPARSE V (auto-enabled on M5) ===
+ggml_metal_library_init: turbo3 sparse V dequant enabled
+| qwen35moe 35B.A3B Q8_0 | 34.36 GiB | 34.66 B | MTL,BLAS | 6 | q8_0 | q8_0 | 1 | tg128 | 84.69 ± 0.38 |
+| qwen35moe 35B.A3B Q8_0 | 34.36 GiB | 34.66 B | MTL,BLAS | 6 | q8_0 | q8_0 | 1 | pp32768+tg128 | 1145.19 ± 2.19 |
+
+=== Q8_0 WITHOUT SPARSE V (TURBO_SPARSE_V=0 force-disabled) ===
+| qwen35moe 35B.A3B Q8_0 | 34.36 GiB | 34.66 B | MTL,BLAS | 6 | q8_0 | q8_0 | 1 | tg128 | 80.69 ± 0.67 |
+| qwen35moe 35B.A3B Q8_0 | 34.36 GiB | 34.66 B | MTL,BLAS | 6 | q8_0 | q8_0 | 1 | pp32768+tg128 | 1096.73 ± 3.39 |
+
+=== SUMMARY ===
+Short decode (tg128): 80.69 → 84.69 tok/s (+5.0%)
+Blended (pp32768+tg128): 1096.73 → 1145.19 tok/s (+4.4%)
+
+Sparse V benefits q8_0 even though q8_0's per-position dequant is much cheaper than turbo3's.
+The skip still saves work at positions with negligible attention weights.