Segment 34

In this sub-session, the assistant 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) which achieved only ~1.5 accept_len and 52 tok/s, revealing misaligned hidden state representations. Phase 1 fine-tuning of the K2 drafter on K2.5 data initially produced random loss due to a critical vocab mapping mismatch; after fixing this, accuracy plateaued at ~38%, converging much slower than the from-scratch model (75% by epoch 5), leading to abandonment of the K2 weight initialization approach. N-gram speculation was then tested but achieved only 41 tok/s, worse than both 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, where 122 NCCL all-reduces per pass consume ~25ms. A comprehensive optimization plan (eagle-fast-verify.md) was created outlining seven priorities to reduce PCIe communication overhead. Execution began with Priority 1A (NCCL_ALGO=Tree) which failed during CUDA graph capture, followed by a two-line code change to enable FlashInfer allreduce fusion for the SM120 Blackwell architecture and updated NCCL tuning with 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 toward reducing verify cost and making speculative decoding viable on this PCIe-bound hardware.

The Verify Wall: When Every Speculative Decoding Path Hits PCIe Physics 3956 words

Chunks

• The Pragmatic Pivot: From Fine-Tuning to PCIe Optimization in EAGLE-3 Speculative Decoding
  2854 words
• The Debugging Spiral: From False Positives to Foundational Understanding in PCIe-Bound LLM Optimization
  2051 words