Agentic inference and RL rollouts need long contexts, cache reuse and fast weight updates.
Ria Jain (@nvidia) shares how Dynamo works with SGLang on KV-aware routing, disaggregated serving and trainer-to-inference weight transfer.
NVIDIA explains how Dynamo works with SGLang for agentic inference
AINVIDIA says Dynamo integrates with SGLang to support KV-aware routing, disaggregated serving, and trainer-to-inference weight transfer. The post targets agentic inference and RL rollouts, which need long contexts, cache reuse, and fast weight updates.
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LMSYS Org · x.comPublished
Original title: Agentic inference and RL rollouts need long contexts, cache reuse and fast weight updates.