Segment 20
After profiling identified AllReduce as the dominant bottleneck, the user pivoted to speculative decoding. The assistant researched draft model options, empirically tested n-gram speculation (9–26% slower due to MoE expert activation overhead), and determined that training a custom EAGLE-3 head (following Baseten's approach) was the most promising path. The assistant then built a complete EAGLE-3 training pipeline on the 8×RTX PRO 6000 hardware, including draft model configuration, dataset preparation, hidden state extraction, vocabulary mapping, and training scripts. End-to-end testing with 10 samples succeeded for dataset preparation and vocabulary mapping but hit runtime errors during hidden state extraction due to API mismatches between the speculators library (designed for vLLM ≤0.15) and the installed vLLM 0.16. The assistant patched the speculators code for the Kimi-K2.5 multimodal wrapper architecture and SchedulerConfig changes, but further KV cache utility API mismatches remained unresolved. The full training plan and pipeline scripts were documented in next-steps-eagle.md and the eagle3-train/ directory.
From AllReduce to EAGLE-3: Building a Speculative Decoding Pipeline for Kimi-K2.5 on Blackwell