Comment on: Experiment: Fused Q·Centroid compressed attention for turbo3 decode

## M2 Pro Results: Bit-Arithmetic Dequant **Hardware:** Apple M2 Pro, Apple8 (1008), has_tensor=false, 32GB **Model:** Qwen2.5-7B-Instruct-Q4_K_M **Build:** experiment/m1-m2-decode-comparison (auto-detected bit-arithmetic) ### Decode Speed (tok/s) | Depth | q8_0 | turbo3 (bit-arith) | Ratio | turbo3 (const LUT, earlier diag) | Ratio | |-------|------|-------------------|-------|----------------------------------|-------| | short | 32.5 | 23.2 | 0.714x | 34.5 | 0.837x | | 4K | 26.0 | 15.7 | 0.604x | 20.4 | 0.640x | | 8K | 22.1 | 11.6 | 0.525x | 14.8 | 0.538x | | 16K | 17.2 | 8.0 | 0.465x | 9.4 | 0.454x | ### Conclusion **Bit-arithmetic did NOT fix the M2 decode cliff.** The ratio still degrades from 0.71x to 0.47x. Worse: bit-arithmetic is **slower than constant LUT at short context** (0.71x vs 0.84x) because the ALU cost exceeds M2's constant cache cost at low contention. **Key finding: The M2 decode bottleneck is NOT the centroid LUT.** The constant cache is not the problem on ...