Segment 16

This sub-session focused on diagnosing and fixing the root cause of incoherent model output, which was traced to two bugs in vLLM's Triton MLA attention backend (output buffer disconnect due to a phantom tensor from a custom PyTorch op) and a shard ordering bug in the GGUF dequantization layer for fused projections. After restoring correct output, performance optimization began: enabling CUDAGraph doubled throughput from ~20 to ~43 tok/s, and tuning NCCL_PROTO=LL further boosted it to ~57 tok/s. Advanced optimizations like custom allreduce and allreduce-RMS fusion were explored but found incompatible with the PCIe-only topology. Finally, the optimized configuration was productionalized into a systemd service (vllm-glm5.service), though initial startup faced a conflict with a stale vLLM process.

Debug Triton MLA attention backend output buffer bugFix GGUF dequantization shard ordering for fused projectionsOptimize throughput with CUDAGraph and NCCL tuningCreate systemd service for vLLM GLM-5 deployment

From Garbage to Production: Debugging, Optimizing, and Deploying GLM-5 on 8× Blackwell GPUs 3471 words

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