| Docs | Roadmap | Recipes | Examples | Containers | Digest | Design Proposals | How to Contribute | Slack |

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.

Events are updated automatically. Subscribe to our public calendar.

• 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:

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