NVIDIA Dynamo 发布博客：面向 Agent 工作负载的 KV 缓存复用方案
如果你的 Agent 跑得慢、GPU 显存被 KV cache 吃满，这篇 NVIDIA Dynamo 的博客讲了 router 加 KV-cache manager 怎么复用上下文，值得照着调优。
PyTorch 官方转发了 NVIDIA 的博客，介绍 NVIDIA Dynamo 如何服务 Agent 工作负载。Agent 场景会在多轮模型调用、工具执行和并行 subagent 中产生持续膨胀的上下文，大量上下文在等待外部工具时仍占用 KV cache。Dynamo 通过集成 router、推理引擎和 KV-cache manager，在并发会话下维持亚秒级延迟目标，同时保留并复用 KV cache 状态。
Agentic workloads generate long, expanding context across repeated model calls, tool execution, and parallel subagents. Much of this context remains stored in the KV cache while an agent waits on external tools or pending operations.
Efficient serving relies on preserving and reusing this PyTorch tensor state while maintaining sub-second latency targets across concurrent sessions.
Read our latest blog from @nvidia to learn how NVIDIA Dynamo integrates its router, inference engine, and KV-cache manager to deliver seamless end-to-end serving for agentic workloads: https://t.co/sf3urp1vzL
