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In this sub-session, the assistant executed a comprehensive benchmark plan for Qwen3.6-27B with DFlash and DDTree speculative decoding on the CT200 machine (8× RTX PRO 6000 Blackwell). After overcoming a machine reboot that required re-downloading the model to `/dev/shm` and fixing a CUDA initialization issue caused by missing LXC cgroup permissions for the `nvidia-uvm` device, the assistant successfully ran TP1, TP4, and TP8 benchmarks. Key findings confirmed that DDTree with a budget of 15 (`b15`) is the optimal configuration, significantly outperforming the DFlash linear baseline and higher budgets (b32, b64) which suffer 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, with TP4 DDTree b15 achieving an aggregate throughput of 1270.8 tok/s at 8 concurrent requests. A LaTeX report generator was created to formalize these results. The user then shifted focus to Kimi K2.6, a pure attention MoE model, to evaluate DDTree without the Mamba state leakage bottleneck. The assistant downloaded the 595 GB model and deployed it with tensor parallelism across all 8 GPUs, resolving a `compressed-tensors` library version mismatch (`nvfp4_pack_quantized`) in the process. The autoregressive baseline for K2.6 showed a similar single-request throughput (~26.3 tok/s) to Qwen3.6 but demonstrated vastly superior batching efficiency, scaling linearly to 807.5 tok/s at 32 concurrent requests. The user also requested testing EAGLE-3 speculative decoding; the drafter was downloaded, but the initial launch attempt failed with an `AssertionError`, indicating a need for further debugging or a different SGLang configuration. A significant portion of the session was dedicated to infrastructure recovery and performance tuning. After a host reboot, the assistant diagnosed and fixed a critical LXC container issue where the `nvidia-uvm` device (major 511) was blocked by cgroup v2 policies, which was preventing CUDA initialization. Host-level PCIe MaxReadReq and NUMA balancing settings were applied, and NCCL environment variables (e.g., `NCCL_PROTO=LL`, `NCCL_ALGO=Ring`) were incorporated into service configurations based on the repository's own tuning guides. The overarching theme is a methodical, data-driven approach to benchmarking speculative decoding on high-end hardware, navigating complex infrastructure challenges, and rapidly pivoting between model architectures (hybrid vs. pure attention) to isolate performance characteristics.

The Blackwell Benchmark Gauntlet: Speculative Decoding, Infrastructure Recovery, and the Pivot to Kimi K2.6 2980 words

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