Primary sourceAnyscale
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Anyscale 工程师直播:用 Ray Serve 部署与优化 LLM 推理
Anyscale 工程师 Marwan 将在一场直播课中讲解 Ray Serve LLM 的部署与扩缩容,覆盖 vLLM 和 SGLang 两种推理引擎。课程内容包括 KV cache、paged attention、prefix caching 和 continuous batching 等推理优化手段,以及 KV-cache-aware routing、prefill/decode 分离和 MoE 模型的 wide expert parallelism 等大规模扩展技术。最后还会演示如何把自托管模型接入 Claude Code、Cursor、Codex 等编程助手,并对比自建 GPU、订阅制与按 token 计费三种方式的成本。
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Anyscale 的工程师要开课讲 Ray Serve 部署 LLM,从 KV cache 优化到接 Cursor、Claude Code 都覆盖,还帮你算自建 GPU 和 API 哪个更省钱。
10 sources39° AI heatLast updated 2026/10/21 17:00:01
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View sourceRelated source 5Akshay Kothari
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View sourceRelated source 7Gorden Sun
View sourceRelated source 8Andrew Curran
View sourceRelated source 9PyTorch
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