Segment 63

This sub-session focused on benchmarking speculative decoding on high-end Blackwell GPUs and pivoting to a new model. The assistant executed a comprehensive benchmark plan for Qwen3.6-27B with DFlash and DDTree on CT200 (8× RTX PRO 6000), overcoming a reboot that required re-downloading the model and fixing an LXC cgroup v2 issue blocking nvidia-uvm device access. Key findings confirmed DDTree budget 15 as optimal, outperforming the linear baseline, while higher budgets suffered from Mamba state leakage inherent to the hybrid Qwen3.6 architecture. TP4 proved faster than TP8 for single requests due to PCIe cross-NUMA overhead, but concurrency scaling was excellent. The assistant then pivoted to Kimi K2.6, a pure attention MoE model, deploying it with TP8 after resolving a compressed-tensors library version mismatch. The autoregressive baseline showed similar single-request throughput to Qwen3.6 but vastly superior batching efficiency. EAGLE-3 speculative decoding was successfully deployed after debugging argument validation errors in SGLang, achieving 1.6–1.7× speedup for single requests, though the advantage narrowed at high concurrency due to PCIe AllReduce bottlenecks. Host-level PCIe MaxReadReq and NUMA balancing settings were applied, and NCCL environment variables were incorporated into service configurations. The user then pivoted to training a DFlash drafter for K2.6, prompting the assistant to search local repositories for DFlash training documentation to prepare for data generation.

Benchmark Qwen3.6 DDTree vs DFlash on BlackwellFix LXC nvidia-uvm cgroup after rebootDeploy Kimi K2.6 autoregressive baselineDebug and deploy EAGLE-3 on K2.6Tune PCIe/NUMA/NCCL for multi-GPUResolve compressed-tensors version mismatchCreate LaTeX benchmark report generatorSearch DFlash training docs for K2.6

The Blackwell Benchmark Gauntlet: From Infrastructure Collapse to Strategic Pivot on Kimi K2.6 5079 words

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