Segment 19
In this sub-session, we performed an extensive 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 investigation of SM120 GEMM optimization strategies including Marlin kernels, L2 cache pinning, persistent fused kernels, and column-major tile scheduling. We executed a three-phase benchmarking plan covering macro-level throughput/latency, 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 that AllReduce accounts for 51.5% of decode time (11.17ms per step), not the GEMMs. 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, beginning research on draft model candidates and vLLM/SGLang support for speculative decoding.