Chunk 36.0

This chunk documented a decisive breakthrough: the successful **CUDA 13 stack upgrade** that finally unblocked Blackwell-native optimizations for the 8× RTX PRO 6000 PCIe system. The assistant navigated significant ABI compatibility challenges to assemble a stable stack (CUDA 13.0.1, PyTorch 2.9.1+cu130, sgl-kernel 0.3.21+cu130, flashinfer 0.6.4, SGLang v0.5.9), yielding an immediate baseline improvement from **89.5 to 92.6 tok/s** (+3.5%) using the FlashInfer attention backend. Beyond the baseline gain, the upgrade directly unblocked the two previously dead-end optimizations. The assistant patched SGLang's `torch_symm_mem` and `kimi_k25.py` modules to properly recognize SM120 (Blackwell). **Both FlashInfer allreduce fusion and Torch symmetric memory now function correctly.** Crucially, FlashInfer allreduce fusion slashed the EAGLE-3 verify pass latency, transforming speculative decoding from a net-negative **54.1 tok/s** (40% slower than baseline) to a net-positive **96.1 tok/s** (3.8% faster than baseline)—a **77.6% improvement** in speculative throughput. Parallel benchmarking revealed that while EAGLE-3 provides a strong advantage at low concurrency (C=1–10), it becomes a liability at higher concurrency (C≥30) where the GPU is already saturated. This led the user to identify the next critical task: implementing **dynamic speculation disabling based on server load**. The session concluded with a clear roadmap to benchmark the baseline at high concurrency to find the exact crossover point, then implement a load-aware speculation policy to maximize overall throughput. The CUDA 13 upgrade transformed the project's trajectory, shifting the bottleneck from "making the verify pass work" to "optimally deploying the working verify pass."

The CUDA 13 Breakthrough: How a High-Stakes Stack Upgrade Transformed Blackwell Speculative Decoding 3210 words

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