Segment 35
In this sub-session, the assistant systematically tested and eliminated several allreduce optimization approaches for the 8× RTX PRO 6000 Blackwell system with PCIe connectivity. 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. 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. 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 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: Methodical Optimization of Blackwell GPUs and the Pivot to CUDA 13