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llm-d 1.0 coordinates distributed inference for bursty agent workloads

cloud.google.com by Mark Lohmeyer, Sean Horgan 9 min read Editor score 8/10

The Kubernetes-native stack pools model servers to address duplicated prefix computation, repeated accelerator state and bursts from multi-step agents. Its design targets serving patterns that generic round-robin load balancing and single-machine deployments struggle with.

What
llm-d 1.0 is an open-source, Kubernetes-native stack that coordinates distributed LLM inference across model servers.
How
Cache-aware routing keeps agent steps on replicas holding their context, tiered KV cache reuses state, and flow control queues bursts.
Why it matters
Session-aware routing and priority controls help agent workloads share accelerators with latency-sensitive requests.
requests routed reques… agents llm-d endpoint cache-awarerouting model servers
Agent requests pass through llm-d routing to the model-server pool. Sketched from the article's own text.

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