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| **[Docs](https://docs.nvidia.com/dynamo/)** | **[Roadmap](https://github.com/ai-dynamo/dynamo/issues/5506)** | **[Recipes](https://github.com/ai-dynamo/dynamo/tree/main/recipes)** | **[Examples](https://github.com/ai-dynamo/dynamo/tree/main/examples)** | **[Prebuilt Containers](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-dynamo/collections/ai-dynamo)** | **[Blog](https://developer.nvidia.com/blog/nvidia-dynamo-1-production-ready/)** | **[Design Proposals](https://github.com/ai-dynamo/enhancements)** |
# 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.
## 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
If you're running a single model on a single GPU, your inference engine alone is probably sufficient.
**Feature support at a glance:**
| | [SGLang](https://docs.nvidia.com/dynamo/backends/sg-lang) | [TensorRT-LLM](https://docs.nvidia.com/dynamo/backends/tensor-rt-llm) | [vLLM](https://docs.nvidia.com/dynamo/backends/v-llm) |
|---|:----:|:----------:|:--:|
| [**Disaggregated Serving**](https://docs.nvidia.com/dynamo/design-docs/disaggregated-serving) | ✅ | ✅ | ✅ |
| [**KV-Aware Routing**](https://docs.nvidia.com/dynamo/components/router) | ✅ | ✅ | ✅ |
| [**SLA-Based Planner**](https://docs.nvidia.com/dynamo/components/planner/planner-guide) | ✅ | ✅ | ✅ |
| [**KVBM**](https://docs.nvidia.com/dynamo/components/kvbm) | 🚧 | ✅ | ✅ |
| [**Multimodal**](https://docs.nvidia.com/dynamo/user-guides/multimodal) | ✅ | ✅ | ✅ |
| [**Tool Calling**](https://docs.nvidia.com/dynamo/user-guides/tool-calling) | ✅ | ✅ | ✅ |
> **[Full Feature Matrix →](https://docs.nvidia.com/dynamo/resources/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](https://inferencex.semianalysis.com/)) |
| **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](https://www.baseten.co/blog/how-baseten-achieved-2x-faster-inference-with-nvidia-dynamo/)) |
| **80%** fewer SLA breaches | Planner autoscaling at 5% lower TCO ([Alibaba APSARA 2025 @ 2:50:00](https://yunqi.aliyun.com/2025/session?agendaId=6062)) |
| **750x** higher throughput | DeepSeek-R1 on GB300 NVL72 ([InferenceXv2](https://inferencex.semianalysis.com/)) |
## 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.
<p align="center">
<img src="./docs/assets/img/dynamo-readme-overview.svg" alt="Dynamo architecture overview" width="600" />
</p>
**[Architecture Deep Dive →](https://docs.nvidia.com/dynamo/design-docs/overall-architecture)**
### Core Capabilities
| Capability | What it does | Why it matters |
|------------|-------------|----------------|
| [**Disaggregated Prefill/Decode**](https://docs.nvidia.com/dynamo/design-docs/disaggregated-serving) | Separates prefill and decode into independently scalable GPU pools | Maximizes GPU utilization; each phase runs on hardware tuned for its workload |
| [**KV-Aware Routing**](https://docs.nvidia.com/dynamo/components/router) | Routes requests based on worker load and KV cache overlap | Eliminates redundant prefill computation — 2x faster TTFT |
| [**KV Block Manager (KVBM)**](https://docs.nvidia.com/dynamo/components/kvbm) | Offloads KV cache across GPU → CPU → SSD → remote storage | Extends effective context length beyond GPU memory |
| [**ModelExpress**](https://github.com/ai-dynamo/modelexpress) | Streams model weights GPU-to-GPU via NIXL/NVLink | 7x faster cold-start for new replicas |
| [**Planner**](https://docs.nvidia.com/dynamo/components/planner/planner-guide) | SLA-driven autoscaler that profiles workloads and right-sizes pools | Meets latency targets at minimum total cost of ownership (TCO) |
| [**Grove**](https://github.com/ai-dynamo/grove) | K8s operator for topology-aware gang scheduling (NVL72) | Places workloads optimally across racks, hosts, and NUMA nodes |
| [**AIConfigurator**](https://github.com/ai-dynamo/aiconfigurator) | Simulates 10K+ deployment configs in seconds | Finds optimal serving config without burning GPU-hours |
| [**Fault Tolerance**](https://docs.nvidia.com/dynamo/user-guides/fault-tolerance/request-migration) | Canary health checks + in-flight request migration | Workers fail; user requests don't |
### New in 1.0
- **Zero-config deploy ([DGDR](https://docs.nvidia.com/dynamo/kubernetes-deployment/deployment-guide/deploying-your-first-model))** *(beta):* Specify model, HW, and SLA in one YAML — AIConfigurator auto-profiles the workload, Planner optimizes the topology, and Dynamo deploys
- **Agentic inference:** Per-request hints for latency priority, expected output length, and cache pinning TTL. [LangChain](https://docs.langchain.com/oss/python/integrations/chat/nvidia_ai_endpoints#use-with-nvidia-dynamo) + [NeMo Agent Toolkit](https://github.com/NVIDIA/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](https://github.com/hao-ai-lab/FastVideo) + [SGLang Diffusion](https://lmsys.org/blog/2026-02-16-sglang-diffusion-advanced-optimizations/) 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
## Quick Start
### Option A: Container (fastest)
```bash
# Pull a prebuilt container (SGLang example)
docker run --gpus all --network host --rm -it nvcr.io/nvidia/ai-dynamo/sglang-runtime:1.0.1
# 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:1.0.1`](https://docs.nvidia.com/dynamo/resources/release-artifacts) and [`vllm-runtime:1.0.1`](https://docs.nvidia.com/dynamo/resources/release-artifacts).
### Option B: Install from PyPI
```bash
pip install "ai-dynamo[sglang]" # or [vllm] or [trtllm]
```
Then start the frontend and a worker as shown above. See the [full installation guide](https://docs.nvidia.com/dynamo/getting-started/quickstart) for system dependencies and backend-specific notes.
### Option C: Kubernetes (recommended)
For production multi-node clusters, install the [Dynamo Platform](https://docs.nvidia.com/dynamo/kubernetes-deployment/deployment-guide) and deploy with a single manifest:
```yaml
# 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 |
|-------|-----------|------|--------|
| Llama-3-70B | vLLM | Aggregated | [View](recipes/llama-3-70b/vllm/) |
| DeepSeek-R1 | SGLang | Disaggregated | [View](recipes/deepseek-r1/sglang/) |
| Qwen3-32B-FP8 | TensorRT-LLM | Aggregated | [View](recipes/qwen3-32b-fp8/trtllm/) |
See [recipes/](recipes/README.md) for the full list. Cloud-specific guides: [AWS EKS](examples/deployments/EKS/) · [Google GKE](examples/deployments/GKE/)
## Building from Source
For contributors who want to build and develop locally. See the [full build guide](https://docs.nvidia.com/dynamo/getting-started/contribution-guide#building-from-source) for details.
```bash
# 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
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`](.devcontainer/README.md) 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](https://docs.nvidia.com/dynamo/getting-started/contribution-guide)** — How to contribute code, docs, and recipes
- **[Design Proposals](https://github.com/ai-dynamo/enhancements)** — RFCs for major features
- **[Office Hours](https://www.youtube.com/playlist?list=PL5B692fm6--tgryKu94h2Zb7jTFM3Go4X)** — Biweekly community calls
- **[Discord](https://discord.gg/D92uqZRjCZ)** — Chat with the team and community
- **[Dynamo Day Recordings](https://nvevents.nvidia.com/dynamoday)** — Deep dives from production users
## Latest News
- [03/15] [Dynamo 1.0 is here — production-ready with strong community adoption](https://developer.nvidia.com/blog/nvidia-dynamo-1-production-ready/)
- [03/15] [NVIDIA Blackwell Ultra sets new inference records in MLPerf](https://developer.nvidia.com/blog/nvidia-blackwell-ultra-sets-new-inference-records-in-mlperf-debut/)
- [03/15] [NVIDIA Blackwell leads on SemiAnalysis InferenceMax benchmarks](https://developer.nvidia.com/blog/nvidia-blackwell-leads-on-new-semianalysis-inferencemax-benchmarks/)
- [12/05] [Moonshot AI's Kimi K2 achieves 10x inference speedup with Dynamo on GB200](https://quantumzeitgeist.com/kimi-k2-nvidia-ai-ai-breakthrough/)
- [12/02] [Mistral AI runs Mistral Large 3 with 10x faster inference using Dynamo](https://www.marktechpost.com/2025/12/02/nvidia-and-mistral-ai-bring-10x-faster-inference-for-the-mistral-3-family-on-gb200-nvl72-gpu-systems/)
- [11/20] [Dell integrates PowerScale with NIXL for 19x faster TTFT](https://www.dell.com/en-us/dt/corporate/newsroom/announcements/detailpage.press-releases~usa~2025~11~dell-technologies-and-nvidia-advance-enterprise-ai-innovation.htm)
<details>
<summary>Older news</summary>
- [11/20] [WEKA partners with NVIDIA on KV cache storage for Dynamo](https://siliconangle.com/2025/11/20/nvidia-weka-kv-cache-solution-ai-inferencing-sc25/)
- [11/13] [Dynamo Office Hours Playlist](https://www.youtube.com/playlist?list=PL5B692fm6--tgryKu94h2Zb7jTFM3Go4X)
- [10/16] [How Baseten achieved 2x faster inference with NVIDIA Dynamo](https://www.baseten.co/blog/how-baseten-achieved-2x-faster-inference-with-nvidia-dynamo/)
- [12/01] [InfoQ: NVIDIA Dynamo simplifies Kubernetes deployment for LLM inference](https://www.infoq.com/news/2025/12/nvidia-dynamo-kubernetes/)
</details>
## Reference
- **[Support Matrix](https://docs.nvidia.com/dynamo/resources/support-matrix)** — Hardware, OS, CUDA, and backend versions
- **[Feature Matrix](https://docs.nvidia.com/dynamo/resources/feature-matrix)** — Detailed backend compatibility
- **[Release Artifacts](https://docs.nvidia.com/dynamo/resources/release-artifacts)** — Containers, wheels, Helm charts
- **[Service Discovery](https://docs.nvidia.com/dynamo/kubernetes-deployment/deployment-guide/service-discovery)** — K8s-native vs etcd vs file-based discovery
- **[Benchmarking Guide](https://docs.nvidia.com/dynamo/user-guides/dynamo-benchmarking)** — Compare deployment topologies with AIPerf
<!-- Reference links for Feature Compatibility Matrix -->
[disagg]: docs/design-docs/disagg-serving.md
[kv-routing]: docs/components/router/README.md
[planner]: docs/components/planner/planner-guide.md
[kvbm]: docs/components/kvbm/README.md
[mm]: examples/multimodal/
[migration]: docs/fault-tolerance/request-migration.md
[lora]: examples/backends/vllm/deploy/lora/README.md
[tools]: docs/agents/tool-calling.md