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Explored NVIDIA Dynamo, a tool for deploying LLMs across multiple GPU cluster nodes with features like model caching, autoscaling, multinode deployments, and Kubernetes integration.
This article summarizes a presentation by Junda Chen on disaggregated inference for LLMs, explaining why goodput (throughput meeting latency SLOs) matters more than raw throughput, and how separating prefill and decode phases improves performance. It also highlights the influence on NVIDIA Dynamo.
This paper presents a game-theoretic analysis of disaggregated inference architectures that separate prefill and decode phases across GPU pools, characterizing how GPU saturation affects performance. The authors propose an adaptive controller that detects saturation transitions and adjusts routing parameters, reducing the Price of Anarchy significantly in experiments on NVIDIA B200 clusters.