新闻
Kimi K3 Performance Optimizations in vLLM: The Road to 2.8× Throughput
vLLM · Wentao Ye, Canlin Guo, Yongye Zhu, Jiangyun Zhu, Ziming Huang, Wei Zhao, Michael Goin, Jie Li · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- vLLM optimized Kimi K3 serving, achieving 2.2, 2.8× throughput and 56, 60% lower latency through targeted kernel and scheduler improvements.
- 为何重要
- Engineers deploying Kimi K3 models at scale who need to maximize token throughput and minimize time-to-first-token in production serving.
- 注意
- Results measured on specific hardware (B300 node) and workload (8K/1K tokens); performance gains vary by concurrency level and may differ on other configurations.
- llm
- kernel
- serving
- throughput
- vllm
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- Latency Optimization
- Agentic Context Engineering (Evolving Playbook)
- Generative UI (Agent-Rendered Interfaces)
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