ai-dynamo/dynamo

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Dynamo

The open-source, datacenter-scale inference stack. Dynamo is the orchestration layer above inference engines — it doesn't replace SGLang, TensorRT-LLM, or vLLM, it turns them into a coordinated multi-node inference system. Disaggregated serving, intelligent routing, multi-tier KV caching, and automatic scaling work together to maximize throughput and minimize latency for LLM, reasoning, multimodal, and video generation workloads.

Built in Rust for performance, Python for extensibility.

Community Events

| Date | Event | Location | |:-----|:------|:---------| | Thu, Sep 10, 2026 | ~~Baseten x Dynamo x SGLang RL post training meetup~~ | Luma | | Mon, Aug 24, 2026 | ~~vLLM x Dynamo meetup~~ | Luma |

Events are updated automatically. Subscribe to our public calendar.

When to use Dynamo

• You're serving LLMs across multiple GPUs or nodes and need to coordinate them
• You want KV-aware routing to avoid redundant prefill computation
• You need to independently scale prefill and decode (disaggregated serving)
• You want automatic scaling that meets latency SLAs at minimum total cost of ownership (TCO)
• You need fast cold-starts when spinning up new replicas

Feature support at a glance:

| | SGLang | TensorRT-LLM | vLLM | |---|:----:|:----------:|:--:| | Disaggregated Serving | ✅ | ✅ | ✅ | | KV-Aware Routing | ✅ | ✅ | ✅ | | SLA-Based Planner | ✅ | ✅ | ✅ | | KVBM | 🚧 | ✅ | ✅ | | Multimodal | ✅ | ✅ | ✅ | | Tool Calling | ✅ | ✅ | ✅ |

Full Feature Matrix → — LoRA, request migration, speculative decoding, and feature interactions.

Key Results

| Result | Context | |--------|---------| | 7x higher throughput per GPU | DeepSeek R1 on GB200 NVL72 w/ Dynamo vs B200 without (InferenceX) | | 7x faster model startup | ModelExpress weight streaming (DeepSeek-V3 on H200) | | 2x faster time to first token | KV-aware routing, Qwen3-Coder 480B (Baseten benchmark) | | 80% fewer SLA breaches | Planner autoscaling at 5% lower TCO (Alibaba APSARA 2025 @ 2:50:00) | | 750x higher throughput | DeepSeek-R1 on GB300 NVL72 (InferenceXv2) |

What Dynamo Does

Most inference engines optimize a single GPU or a single node. Dynamo is the orchestration layer above them — it turns a cluster of GPUs into a coordinated inference system.

Architecture Deep Dive →

Core Capabilities

| Capability | What it does | Why it matters | |------------|-------------|----------------| | Disaggregated Prefill/Decode | Separates prefill and decode into independently scalable GPU pools | Maximizes GPU utilization; each phase runs on hardware tuned for its workload | | KV-Aware Routing | Routes requests based on worker load and KV cache overlap | Eliminates redundant prefill computation — 2x faster TTFT | | KV Block Manager (KVBM) | Offloads KV cache across GPU → CPU → SSD → remote storage | Extends effective context length beyond GPU memory | | ModelExpress | Streams model weights GPU-to-GPU via NIXL/NVLink | 7x faster cold-start for new replicas | | Planner | SLA-driven autoscaler that profiles workloads and right-sizes pools | Meets latency targets at minimum total cost of ownership (TCO) | | Grove | K8s operator for topology-aware gang scheduling (NVL72) | Places workloads optimally across racks, hosts, and NUMA nodes | | AISimulate | Predicts serving behavior and searches deployment configurations offline | Finds a strong serving configuration without bringing up a GPU cluster | | Fault Tolerance | Canary health checks + in-flight request migration | Workers fail; user requests don't |

New in 1.0

• Zero-config deploy (DGDR) (beta): Specify model, HW, and SLA in one YAML — AISimulate performance models estimate candidate configurations, Planner optimizes the topology, and Dynamo deploys
• Agentic inference: Per-request hints for priority, expected output length, and speculative prefill, plus session metadata for tracing and SGLang subagent KV isolation. LangChain + NeMo Agent Toolkit integrations
• Multimodal E/P/D: Disaggregated encode/prefill/decode with embedding cache — 30% faster TTFT on image workloads
• Video generation: Native FastVideo + SGLang Diffusion support — real-time 1080p on single B200
• K8s Inference Gateway plugin: KV-aware routing inside the standard Kubernetes gateway
• Storage-tier KV offload: S3/Azure blob support + global KV events for cluster-wide cache visibility

Request Routing Topologies

Dynamo can expose traffic through two Kubernetes request routing topologies. Both expose an OpenAI-compatible API and support the same backends, disaggregated serving, and KV-aware routing.

| Topology | What it is | When to use | |------|------------|-------------| | Dynamo-native Frontend routing | The Dynamo Frontend serves HTTP and the integrated Dynamo Router makes worker-selection decisions. No external gateway is required. | Local development, single-cluster deployments, and environments where Dynamo should own the request entry point end to end. | | Gateway API routing with GAIE | A Kubernetes Gateway API Inference Extension gateway calls the Dynamo Endpoint Picker Plugin (EPP) before forwarding to the selected worker's Frontend sidecar in --router-mode direct. | Kubernetes platforms that standardize on Gateway API, or deployments where gateway-level policy, auth, rate limiting, and observability should sit at the cluster edge. |

Request flow for the Dynamo-native path is client → Frontend → Router → workers. Request flow for the Gateway API path is client → Gateway → EPP → Frontend sidecar (direct) → workers.

See the Gateway API Inference Extension (GAIE) guide for the Gateway API setup, supported features, and configuration.

Quick Start

This repo ships agent skills: if you work with an AI coding agent (Claude Code, Codex,

Cursor), clone the repo and ask it to deploy, troubleshoot, benchmark, or optimize a Dynamo

deployment. The skills activate automatically; no setup required.

Option A: Container (fastest)

# Pull a prebuilt container (SGLang example)
docker run --gpus all --network host --rm -it nvcr.io/nvidia/ai-dynamo/sglang-runtime:1.4.2
Inside the container — start frontend and worker
python3 -m dynamo.frontend --http-port 8000 --discovery-backend file > /dev/null 2>&1 &
python3 -m dynamo.sglang --model-path Qwen/Qwen3-0.6B --discovery-backend file &
Send a request
curl -s localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
  "model": "Qwen/Qwen3-0.6B",
  "messages": [{"role": "user", "content": "Hello!"}],
  "max_tokens": 100
}' | jq

Also available: tensorrtllm-runtime:1.4.2 and vllm-runtime:1.4.2.

Option B: Install from PyPI

Install uv (curl -LsSf https://astral.sh/uv/install.sh | sh), then:

uv pip install --prerelease=allow "ai-dynamo[sglang]"   # or [vllm]

Note: TensorRT-LLM requires pip with --extra-index-url https://pypi.nvidia.com. See the install guide for TRT-LLM-specific instructions.

Then start the frontend and a worker as shown above. See the full installation guide for system dependencies and backend-specific notes.

Option C: Kubernetes (recommended)

For production multi-node clusters, install the Dynamo Platform and deploy with a single manifest:

# Zero-config deploy: specify model + SLA, Dynamo handles the rest
apiVersion: nvidia.com/v1beta1
kind: DynamoGraphDeploymentRequest
metadata:
  name: my-model
spec:
  model: Qwen/Qwen3-0.6B
  backend: vllm
  sla:
    ttft: 200.0   # ms
    itl: 20.0     # ms
  autoApply: true

Pre-built recipes for common models:

| Model | Framework | Mode | Recipe | |-------|-----------|------|--------| | Qwen3-32B-FP8 | TensorRT-LLM | Aggregated | View | | DeepSeek-R1 | SGLang | Disaggregated | View | | Kimi-K3 | vLLM | Aggregated | View |

See recipes/ for the full list. Cloud-specific guides: AWS EKS · Google GKE · Azure AKS · Amazon ECS

Building from Source

For contributors who want to build and develop locally. See the full build guide for details.

# Install system deps (Ubuntu 24.04)
sudo apt install -y build-essential libhwloc-dev libudev-dev pkg-config libclang-dev protobuf-compiler python3-dev cmake
Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh && source $HOME/.cargo/env
Create venv and build
uv venv dynamo && source dynamo/bin/activate
uv pip install pip 'maturin[patchelf]'
cd lib/bindings/python && maturin develop --uv && cd $PROJECT_ROOT
uv pip install -e lib/gpu_memory_service
uv pip install -e .

VSCode/Cursor users: see the .devcontainer for a pre-configured dev environment.

Community and Contributing

Dynamo is built in the open with an OSS-first development model. We welcome contributions of all kinds.

• Contribution Guide — How to contribute code, docs, and recipes
• Design Proposals — RFCs for major features, tracked as dep:* labeled GitHub issues
• Office Hours — Biweekly calls
• Community Meetings (Youtube) – Weekly (Wed 10:30 AM PT) development community meetings
• Slack — Chat with the team and community
• Dynamo Day Recordings — Deep dives from production users

Latest News

• [03/15] Dynamo 1.0 is here — production-ready with strong community adoption
• [03/15] NVIDIA Blackwell Ultra sets new inference records in MLPerf
• [03/15] NVIDIA Blackwell leads on SemiAnalysis InferenceMax benchmarks
• [12/05] Moonshot AI's Kimi K2 achieves 10x inference speedup with Dynamo on GB200
• [12/02] Mistral AI runs Mistral Large 3 with 10x faster inference using Dynamo
• [11/20] Dell integrates PowerScale with NIXL for 19x faster TTFT

cargo run -p dynamo-llm --bin generate-frontend-openapi

Reference

• Support Matrix — Hardware, OS, CUDA, and backend versions
• Feature Matrix — Detailed backend compatibility
• Release Artifacts — Containers, wheels, Helm charts
• Service Discovery — K8s-native vs etcd vs file-based discovery
• Benchmarking Guide — Compare deployment topologies with AIPerf

[disagg]: docs/design-docs/disagg-serving.md [kv-routing]: docs/components/router/README.md [planner]: docs/fern/pages/developer-guide/knowledge-base/modular-components/planner/planner-guide.md [kvbm]: docs/components/kvbm/README.md [migration]: docs/fault-tolerance/request-migration.md [lora]: examples/backends/vllm/deploy/lora/README.md [tools]: docs/fern/pages/use-cases/tool-calling-and-reasoning/tool-call-parsing.mdx