Chunk 64.0

## Summary This chunk focused on three major themes: infrastructure recovery, parallelism optimization, and DFlash speculative decoding deployment for Kimi K2.6. The assistant first resolved a cascade of CUDA toolkit issues—FlashInfer's SM120 rejection on Blackwell GPUs, missing `curand.h` headers, and JIT compilation failures—by cleanly installing the full CUDA 13.0 toolkit alongside the system's existing CUDA 12.8, then patching SGLang's triton attention backend to support pipeline parallelism with non-zero start layers. This unblocked all downstream work. The assistant then systematically benchmarked four parallelism strategies across 8× RTX PRO 6000 (PCIe-only): TP8 (pure tensor parallelism), PP8 (pipeline parallelism), EP8 (expert parallelism), and EP4 (expert parallelism with TP2 groups). EP8 dramatically improved single-request throughput from 26→65 tok/s by eliminating AllReduce on MoE layers, and EP4 reached peak aggregate throughput of ~1531 tok/s at high concurrency. PP8 was disappointing due to pipeline bubbles, while TP8 with CUDA graphs hit 98 tok/s at C=1 but plateaued at ~1291 tok/s. The user's intuition that expert parallelism avoids PCIe AllReduce bottlenecks was validated—EP configurations won across the board. Finally, the assistant downloaded `SubSir/Kimi-K2.6-DFlash-tmp-long` (6.5 GB, block_size=8, 6 draft layers) and deployed it with SGLang's DFlash speculative decoding on EP8. Acceptance length reached 3.5–4.1 tokens per step (35–44% rate), yielding 86 tok/s at C=1 (1.3× over EP8 baseline) but lower peak throughput (~1146 tok/s) than autoregressive EP8 due to CUDA graph incompatibility with the DFlash verify path. The chunk concluded with the user asking deep architectural questions about DFlash's memory bandwidth efficiency, compute-vs-verify tradeoffs, and whether DDTree changes the parallelism calculus—setting up the next phase of optimization.

The Optimization Gauntlet: From CUDA Toolkit Recovery to Parallelism Benchmarking and DFlash Deployment for Kimi K2.6 2193 words

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