How NVIDIA Dynamo Boosts Multi‑Node Distributed Inference MFU for Agentic AI
The article explains how NVIDIA Dynamo tackles the production bottlenecks of Agentic AI by using KV‑Cache‑aware routing, a three‑stage multimodal inference architecture, and intelligent cache scheduling on Kubernetes to improve multi‑node throughput (MFU) while maintaining latency SLAs.
Agentic AI workloads—long‑running agents, parallel sub‑tasks, and multimodal inputs—stress traditional single‑node inference stacks.
Inference challenge for long‑running agents : In extended agent workflows, tool‑chain calls and parallel sub‑agents generate many shared KV‑Cache prefixes. Precise routing is required to reuse cache and to recover sessions seamlessly after GPU failures. NVIDIA Dynamo addresses this by making KV‑Cache‑aware routing decisions and using a declarative RoleBasedGroup mechanism, enabling stateful agent services to run highly available on Kubernetes.
Three‑stage separation for multimodal inference : Video generation and multimodal apps need heterogeneous compute for image encoding, pre‑fill, and decoding. Dynamo 1.1 splits the pipeline into Embedding, Prefill, and Decode stages and introduces an Embedding Cache, which raises throughput and resource utilization without additional hardware.
Intelligent KV‑Cache scheduling and offloading : A multi‑level KV‑Cache offload combined with an SLA Planner lets the system predict load and model performance, dynamically adjusting the number of Prefill and Decode instances. This meets latency SLAs while minimizing deployment cost.
The presentation also shows how to deploy NVIDIA Dynamo for Agentic AI on Kubernetes, offering concrete architecture designs and tuning practices.
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