Chunk 20.0

After the deep profiling session revealed AllReduce as the dominant bottleneck (51.5% of decode time), the user pivoted to investigate speculative decoding as a potential throughput optimization. The assistant launched parallel research agents covering speculative decoding fundamentals, vLLM/SGLang framework support, candidate draft models, and training feasibility. The research uncovered that n-gram speculation is poorly suited for reasoning models (which generate novel thinking chains with little repetition), that the only viable off-the-shelf draft model is `AQ-MedAI/Kimi-K2-Instruct-eagle3` (trained for K2, not K2.5, so acceptance rates would be lower), and that the Baseten approach of training a custom EAGLE-3 head is the most promising path. The assistant empirically tested n-gram speculation on the running vLLM instance and confirmed it was **9–26% slower** than baseline due to MoE expert activation overhead during verification — exactly as predicted by recent MoE-Spec research. The user then directed the assistant to begin implementing the EAGLE-3 training pipeline on the existing 8×RTX PRO 6000 hardware, with the understanding that the hero run would be ported to rented B200/B300 NVL8 machines. The assistant built a complete training pipeline consisting of: a draft model configuration (matching the K2 EAGLE-3 architecture with 32K draft vocabulary), a dataset preparation script using HuggingFace datasets, a hidden state extraction script using speculators' `VllmHiddenStatesGenerator`, a vocabulary mapping script, a training script using speculators' trainer, and a shell orchestrator. The pipeline was tested end-to-end with 10 samples from `mlabonne/open-perfectblend`: dataset preparation and vocabulary mapping succeeded, but hidden state extraction hit runtime errors due to API mismatches between speculators (designed for vLLM ≤0.15) and the installed vLLM 0.16. The assistant patched the speculators code to handle the Kimi-K2.5 multimodal wrapper architecture (`model.language_model.model.layers` instead of `model.model.layers`) and to fix `SchedulerConfig` parameter changes, but further KV cache utility API mismatches remained at the chunk's end. The full training plan and pipeline scripts were documented in `next-steps-eagle.md` and the `eagle3-train/` directory.

The Speculative Deciding: How Empirical Testing Killed N-Gram Speculation and Forged an EAGLE-3 Training Pipeline for Kimi-K2.5 3005 words

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