NVIDIA Dynamo — Distributed Inference Framework for Large-Scale AI Models

Last updated: 11/11/2025

NVIDIA Dynamo

Published: November 11, 2025

NVIDIA Dynamo is an open-source, high-throughput, low-latency inference framework from NVIDIA designed to deploy, scale, and optimize AI model inference across multi-node, multi-GPU environments. :contentReference[oaicite:2]{index=2}

Key Features

  • Distributed, disaggregated serving: Separates pre-fill (context ingestion) and decode (token generation) phases across distinct GPU pools to optimize throughput. :contentReference[oaicite:3]{index=3}
  • LLM-aware request routing: Routes inference requests to GPUs based on existing KV cache overlap to avoid redundant computation. :contentReference[oaicite:4]{index=4}
  • KV-cache offloading: Moves key/value (KV) cache from GPU HBM to lower-cost memory/storage tiers (CPU RAM, SSD, network) to increase utilization and reduce cost. :contentReference[oaicite:5]{index=5}
  • Dynamic GPU resource planner: Monitors workload metrics (request type, sequence length, latency) and dynamically allocates/de‐allocates GPU workers for optimal efficiency. :contentReference[oaicite:6]{index=6}
  • Topology-aware communication library (NIXL): Accelerates data movement and coordination across heterogeneous hardware (GPUs, CPUs, storage) in large clusters. :contentReference[oaicite:7]{index=7}
  • Framework & backend agnostic: Supports major inference backends such as PyTorch, vLLM, TensorRT-LLM, SGLang, enabling integration into existing stacks. :contentReference[oaicite:8]{index=8}

Latest Update Highlights

  • Launched publicly in 2025 as the successor to NVIDIA Triton Inference Server, geared toward large reasoning/agentic-AI models. :contentReference[oaicite:10]{index=10}
  • Benchmarks: On the GB200 NVL72 system, Dynamo reported up to 30× higher throughput for MoE models compared to previous platforms. :contentReference[oaicite:11]{index=11}
  • Cloud recipes: For example, deploying Dynamo on Google Cloud’s AI Hypercomputer (H200 GPUs on GKE) demonstrated substantial latency and cost benefits. :contentReference[oaicite:13]{index=13}

Use Cases

  • Serving reasoning or large-token-generation models (e.g., MoE, agentic workflows) at data-center scale
  • Production deployment of inference for multi‐turn conversational agents with large context windows
  • Kubernetes-native AI inference orchestration in cloud/edge clusters
  • Deployments requiring dynamic load scaling in response to variable request patterns

Workflow Overview

  1. Clone or install the Dynamo repository from GitHub (open-source) and review the user guide. :contentReference[oaicite:14]{index=14}
  2. Configure cluster-level serving: define GPU pools for prefill and decode, set up KV cache tiers, and enabling the Smart Router.
  3. Deploy your model (via supported backends) and define serving strategy (e.g., disaggregated vs aggregated) via Dynamo’s Planner.
  4. Monitor real-time metrics (first‐token latency, inter-token latency, utilization, queue wait) and fine-tune resource allocations.
  5. In production, use routing logic and autoscaling to dynamically adjust GPU workers, KV cache offload thresholds, and inter-node communications.
  6. Collect logs, monitor throughput and cost-efficiency, iterate configurations, and publish for production workloads.

Developer Resources

  • Documentation: NVIDIA Dynamo Documentation :contentReference[oaicite:15]{index=15}
  • GitHub: ai-dynamo/dynamo (Apache-2.0 licensed) :contentReference[oaicite:16]{index=16}
  • Technical blog: “Introducing NVIDIA Dynamo – A low-latency distributed inference framework” (Mar 18 2025) :contentReference[oaicite:17]{index=17}
  • Cloud case-study: Deploying on Google Cloud (Sept 10 2025) :contentReference[oaicite:18]{index=18}

Platform & Ecosystem Context

  • Positioned as the next-gen inference serving layer for large-scale generative AI, replacing older frameworks and optimized for “AI factories”. :contentReference[oaicite:19]{index=19}
  • Designed to scale from single GPU setups to thousands of GPUs across nodes and clusters, with routing, cache reuse, and resource planning built-in. :contentReference[oaicite:20]{index=20}
  • Integrates with NVIDIA’s wider ecosystem (AI Enterprise, NIM microservices, Cloud/GPU providers) and open backend tool-chains. :contentReference[oaicite:21]{index=21}

Category & Publisher

Category: Distributed AI Inference Framework
Publisher: NVIDIA
Open Source License: Apache 2.0
Release Year: 2025