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AI CLUSTERED EVENT · 10/7/2026

vLLM Optimizes DeepSeek-V4.1-Flash: 5.3x Agentic Throughput via New Kernels and CUDA Graphs

1 reports archived1 independent sourcesupdated 10/7/2026, 08:00:00
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Inferact and the vLLM community achieved a 5.3x throughput improvement for DeepSeek-V4.1-Flash in agentic scenarios within three weeks of its release. Key optimizations include integrating DeepSeek's new kernels (MegaAttention, Mega-mHC) and implementing SWA bounded replay with CUDA graphs to reduce Time-To-First-Token by ~30%. Aggressive kernel fusion and parallelization further unlocked the memory efficiency of the model's causal encoder-decoder architecture.

LATEST/Inferact and the vLLM community achieved a 5.3x throughput improvement for DeepSeek-V4.1-Flash in agentic scenarios within three weeks of its release. Key optimizations include integrating DeepSeek's new kernels (MegaAttention, Mega-mHC) and implementing SWA bounded replay with CUDA graphs to reduce Time-To-First-Token by ~30%. Aggressive kernel fusion and parallelization further unlocked the memory efficiency of the model's causal encoder-decoder architecture.

TIMELINECoverage timeline

Total 1 reports · Latest first
  1. vLLM 官方博客T1·68 pts

    vLLM Optimizes DeepSeek-V4.1-Flash: 5.3x Agentic Throughput via New Kernels and CUDA Graphs

    Original: DeepSeek-V4.1-Flash on vLLM: 5x Agentic Throughput Since Day 0

    • DeepSeek-V4.1-Flash achieves 5.3x throughput gain on vLLM, with 1.9x speedup at low concurrency.
    • Integration of new kernels like MegaAttention and SWA bounded replay significantly reduces TTFT.
    • Leveraging the causal encoder-decoder architecture with FP4 KV Cache drastically compresses memory footprint.