Chunk 35.0

In this chunk, the assistant systematically tested and eliminated several allreduce optimization approaches for the PCIe-connected 8×RTX PRO 6000 Blackwell system. FlashInfer allreduce fusion failed because its JIT compiler does not support SM120 (Blackwell) architecture. The custom allreduce kernel, when forced to work on PCIe, produced only 38 tok/s—more than 2× slower than NCCL—due to massive PCIe bus contention from the all-to-all communication pattern. Torch symmetric memory also failed because SM120 is not in its architecture lookup table. Expert Parallelism with the flashinfer A2A backend hit an assertion error and OOM, making it non-functional. These results confirmed that NCCL Ring remains the best allreduce strategy for this hardware. A key achievement was the discovery that reducing `--cuda-graph-max-bs` from 512 to 128 improved the baseline throughput from 82 to 89.5 tok/s—a 9% gain—by freeing GPU memory for KV cache. However, EAGLE-3 speculative decoding still only reached 54.1 tok/s, well below the baseline, because the verify pass bottleneck (~30ms for 122 NCCL allreduces) remained unresolved. The assistant then updated the optimization plan document with all experimental results. The user proposed upgrading CUDA to version 13, which has native SM120 support, to potentially unblock the dead ends. The assistant confirmed that the driver already supports CUDA 13.1, but the toolkit is only 12.8. Research revealed that PyTorch nightly provides cu130 wheels, sgl-kernel has a dedicated cu130 index, and flashinfer also supports CUDA 13. This upgrade path could enable flashinfer fusion, torch symmetric memory, and other Blackwell-native optimizations that were previously unavailable, offering a promising direction to finally reduce the verify cost and make speculative decoding profitable.

The Systematic Elimination: How an Optimization Campaign for Blackwell GPUs Navigated Dead Ends to Discover the CUDA 13 Path 2280 words

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