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In this session, the user systematically attempted to improve Kimi-K2.5 inference throughput via speculative decoding, starting with a direct probe of the AQ-MedAI K2 EAGLE-3 drafter (Phase 0). This achieved an accept_len of ~1.5 and 52 tok/s, confirming architectural compatibility but revealing misaligned hidden state representations. Phase 1 (fine-tuning the K2 drafter on K2.5 data) initially produced random loss (~18-20), which the assistant diagnosed as a critical vocab mapping mismatch—only 252 out of 32,000 draft-to-target token positions matched between the AQ-MedAI and K2.5 mappings. Fixing this dropped the loss to ~9 and improved accuracy to ~24%. However, the fine-tuning plateaued at ~38% accuracy, converging slower than the from-scratch model (which reached 75% by epoch 5), leading to the conclusion that the K2 weights were a poor initialization for K2.5 and the approach was abandoned. With the K2 fine-tuning path closed, the user explored simpler alternatives. N-gram speculation was tested but achieved only 41 tok/s due to its expensive tree-verify overhead, performing worse than both the baseline (82 tok/s) and the existing EAGLE-3 drafter (60 tok/s). The assistant then pivoted to the fundamental bottleneck: the ~30ms verify step, which is 97% of the EAGLE-3 cycle time. A deep analysis of the SGLang verify path revealed that 122 NCCL all-reduces per pass consume ~25ms of the 30ms, with actual compute being only ~5ms. This led to the creation of `eagle-fast-verify.md`, a comprehensive plan outlining seven optimization priorities to reduce PCIe communication overhead. The assistant began executing the plan immediately. Priority 1A (NCCL_ALGO=Tree) failed during CUDA graph capture. The assistant then applied a two-line code change to enable FlashInfer allreduce fusion for the SM120 Blackwell architecture and updated the NCCL tuning to use fewer channels and a smaller buffer. The server was launched with these combined changes to benchmark the impact on baseline throughput, representing the first concrete step towards reducing the verify cost and making speculative decoding viable on this PCIe-bound hardware. The overarching theme of the session was a pragmatic pivot from data-centric improvements (more training data, fine-tuning) to system-level communication optimization, driven by the stark reality that PCIe allreduce latency is the dominant bottleneck.

The Pragmatic Pivot: From Fine-Tuning to PCIe Optimization in EAGLE-3 Speculative Decoding 2854 words

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