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This sub-session began by migrating the Qwen3.6-27B deployment from kpro6 to kpro5, fixing SGLang version compatibility for GDN hybrid attention, and achieving 73.5 tok/s with MTP speculation. The focus then shifted to DFlash speculative decoding, where the assistant acquired the drafter, deployed vLLM with DFlash, but encountered catastrophically low acceptance rates. Deep investigation revealed three root causes: a layer-ID offset missing in vLLM's hidden state extraction, sliding window attention layers being ignored, and eagle cache drop issues. The assistant installed vLLM from unmerged PR #40898 to address these. Attempts to implement DDTree tree-based verification hit an architectural limitation in vLLM's linear-chain rejection sampler, leading to a pivot toward training a better drafter. A comprehensive 913K-sample dataset was curated, tokenized, and a training environment was set up on an 8× RTX PRO 6000 Blackwell node. The final major effort was building a custom offline hidden state extraction pipeline using HuggingFace Transformers, achieving a 20× throughput improvement (from 7–11 to 140–155 samples/s per GPU) through GPU-side batching, async S3 uploads, and robust monitoring, with extraction running at high utilization.

Migrate Qwen3.6-27B deployment to kpro5 hostFix SGLang version for GDN hybrid attentionInvestigate DFlash speculative decoding low acceptance rateIdentify vLLM DFlash integration bugs (layer-ID offset, SWA layers)Evaluate DDTree standalone code for Qwen3.6-27BCurate 913K-sample training dataset for DFlash drafterBuild and optimize offline hidden state extraction pipelineSet up training environment on 8× Blackwell node

From Node Migration to Custom Training: The Complete Arc of Speculative Decoding Optimization for Qwen3.6-27B 3267 words

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