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## Summary In this chunk, we conducted a comprehensive deep-dive profiling campaign to understand the exact performance bottlenecks of Kimi-K2.5 INT4 on the 8x RTX PRO 6000 Blackwell system. After the user questioned whether overlapping allreduce with compute was feasible, we researched disaggregated prefill and data parallelism options in vLLM/SGLang, concluding that the real bottleneck was not communication but rather the tiny MoE expert GEMMs caused by TP=8 sharding. This led to an extensive investigation of SM120 GEMM optimization strategies, including Marlin kernels, L2 cache pinning, persistent fused kernels, and column-major tile scheduling. We then executed a three-phase benchmarking plan: macro-level throughput/latency tests against the running vLLM server, micro-benchmarks of individual GEMM operations at exact Kimi-K2.5 dimensions, NCCL AllReduce burst measurements, and a full torch.profiler capture. The profiling revealed a surprising result: **AllReduce accounts for 51.5% of decode time** (11.17ms per step), not the GEMMs. Marlin W4A16 kernels eliminated the dtype-cast overhead that plagued the earlier GLM-5 NVFP4 runs, but this unmasked the fundamental PCIe allreduce bottleneck. Expert Parallelism was considered but the user correctly noted that without NVLink, all-to-all on PCIe would face serialization issues at higher batch sizes, and declined precision-compromising approaches. The session concluded with the user deciding to restart the current production setup, and then pivoting to investigate **speculative decoding** as a potential path to improve throughput without hardware changes. We began researching draft model candidates and vLLM/SGLang support for speculative decoding, creating parallel research agents to explore draft models like Qwen2.5-7B, DeepSeek-R1-Distill, and the possibility of training a custom distilled draft model. The overarching theme is the tension between model quality (refusing to compromise precision) and the hard physical constraints of PCIe-only multi-GPU inference, with speculative decoding emerging as the most promising software-only optimization to explore.

The Optimization Odyssey: How a 1T-Parameter MoE Deployment on 8x Blackwell GPUs Revealed the True Cost of PCIe Inference 2631 words

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