Why Agentic AI Inference Is Slow and How NVIDIA Dynamo 1.1 Solves It

When deploying Agentic AI, developers often encounter severe latency: each multi‑turn conversation may involve tens of thousands of tokens that are recomputed from scratch, KV‑cache entries are evicted by LRU policies during tool‑call waits, and sub‑agents experience cold‑starts with shared prefixes recomputed on different GPUs, leading to low GPU utilization and poor user experience. These problems stem from traditional inference infrastructures being designed for single‑turn dialogs, whereas Agentic AI workloads repeatedly reuse system prompts and tool definitions each turn, have zero token reuse during inference, and share prefixes across nodes.

NVIDIA Dynamo 1.1 is introduced as a production‑grade, multi‑node AI inference framework that directly tackles these challenges. It implements KV‑cache‑aware routing to enable cross‑node prefix reuse, employs multi‑level KV‑cache offloading that extends caching from GPU memory to host memory and storage, uses intelligent priority scheduling to protect critical sessions from eviction, and supports a Prefill/Decode separation deployment model that eliminates resource contention between token generation phases.

An upcoming live technical session will be held on June 25 at 19:00 as part of the NVIDIA AI Acceleration Lecture series, titled “Scaling Agentic AI Inference: NVIDIA Dynamo and Its Kubernetes Inference Practice.” The session will provide an in‑depth walkthrough of the open‑source Dynamo framework, demonstrate Agentic AI inference deployment on Kubernetes using Dynamo and RBG, and discuss how Dynamo supports multimodal and video‑generation workloads at scale. Interested participants can reserve a free spot via the provided QR code.