Chunk 67.1

In this chunk, the assistant executed a methodical, measurement-driven optimization campaign on the DeepSeek-V4-Flash deployment. After pulling the latest upstream SGLang, generating SM120 FP8 GEMM autotune configs, and enabling MTP/EAGLE speculative decoding, the assistant benchmarked each lever individually. The FP8 configs gave only ~6% improvement (since FP8 GEMM is only 6% of decode), NCCL LL had negligible effect (communication is 2%), and MTP boosted single-request throughput by 47% but provided zero gain at concurrency due to verifier saturation. A C=16 profile revealed the decisive bottleneck: the MoE slot-GEMV and sparse-decode attention kernels consumed 39% and 38% of GPU time respectively, both running on CUDA cores (SIMT) rather than the sm_120a FP4/FP8 tensor cores, explaining the user's observation of <1% tensor-pipe utilization. The assistant then pivoted to the highest-leverage fix: switching from the stock MXFP4 checkpoint to the official NVIDIA NVFP4 quantization, which routes MoE execution through tensor-core paths (Marlin W4A16 or native cutlass FP4 grouped GEMM). PR #25820 was fetched and applied for correct NVFP4 auto-detection, and the 149 GB checkpoint was downloaded. Both NVFP4 backends delivered identical throughput (~28 tok/s at C=16, a ~24% improvement over baseline), confirming that MoE was now on tensor cores but attention remained the dominant bottleneck. Crucially, the user noted that the hardware should be capable of 300–600 tok/s at C=16 based on roofline analysis (1.9 TB/s VRAM bandwidth, 13B active parameters), and the assistant discovered that the MTP verifier was consuming enough GPU memory to halve the effective batch size from 16 to 8, explaining the large throughput gap. The core theme is that configuration tuning and even checkpoint format changes can only deliver incremental gains against a structural bottleneck: the sm_120 fallback kernels for sparse-MLA decode and MXFP4 MoE are fundamentally inefficient, running on CUDA cores instead of tensor cores. The NVFP4 checkpoint fixed the MoE path, but attention remains the primary limiter. The path forward requires either disabling MTP to restore batch capacity and re-measuring, or building a split-K tensor-core sparse-attention kernel to attack the remaining 38% bottleneck—the same playbook that delivered 3–6× gains in the earlier K2.6 work.

The Methodical Pursuit of Performance: Optimizing DeepSeek-V4-Flash on Blackwell GPUs 2241 words

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