GeezerVison is showing docs.nvidia.com — Dynamo | NVIDIA Dynamo Documentation Skip to navigationThis page is for version Latest (v1.6.1) (default).
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- Latest (v1.6.1) (default): https://docs.nvidia.com/dynamo/latest/llms.txt
- dev: https://docs.nvidia.com/dynamo/dev/llms.txt
- v1.6.1: https://docs.nvidia.com/dynamo/v1.6.1/llms.txt
- v1.6.0: https://docs.nvidia.com/dynamo/v1.6.0/llms.txt
- v1.5.1: https://docs.nvidia.com/dynamo/v1.5.1/llms.txt
- v1.5.0: https://docs.nvidia.com/dynamo/v1.5.0/llms.txt
- v1.4.2: https://docs.nvidia.com/dynamo/v1.4.2/llms.txt
- v1.4.1: https://docs.nvidia.com/dynamo/v1.4.1/llms.txt
- v1.4.0: https://docs.nvidia.com/dynamo/v1.4.0/llms.txt
- v1.3.0: https://docs.nvidia.com/dynamo/v1.3.0/llms.txt
- v1.2.1: https://docs.nvidia.com/dynamo/v1.2.1/llms.txt
- v1.2.0: https://docs.nvidia.com/dynamo/v1.2.0/llms.txt
- v1.1.1: https://docs.nvidia.com/dynamo/v1.1.1/llms.txt
- v1.1.0: https://docs.nvidia.com/dynamo/v1.1.0/llms.txt
- v1.0.2: https://docs.nvidia.com/dynamo/v1.0.2/llms.txt
- v1.0.1: https://docs.nvidia.com/dynamo/v1.0.1/llms.txt
- v1.0.0: https://docs.nvidia.com/dynamo/v1.0.0/llms.txt
- v0.9.1: https://docs.nvidia.com/dynamo/v-0-9-1/llms.txt
- v0.9.0: https://docs.nvidia.com/dynamo/v-0-9-0/llms.txt
- v0.8.1: https://docs.nvidia.com/dynamo/v-0-8-1/llms.txt
- v0.8.0: https://docs.nvidia.com/dynamo/v-0-8-0/llms.txt
- v0.7.1: https://docs.nvidia.com/dynamo/v-0-7-1/llms.txt
- v0.7.0: https://docs.nvidia.com/dynamo/v-0-7-0/llms.txt
Dynamo works with vLLM, SGLang, and TensorRT-LLM. Dynamo works with vLLM, SGLang, and TensorRT-LLM; supports NVIDIA and AMD GPUs and Intel XPUs; runs on Kubernetes, Slurm, or locally; and scales one component or the full stack. Deployment walkthrough See Dynamo in actionDynamoQwen3-235B deploymentDemo runningShow playback controls 01 · Distributed inference Scale the system around the modelDisaggregated serving, KV-aware routing, cache management, and autoscaling coordinate the full inference pipeline. Dynamo Metrics Time to first token↓ Request throughput↑ KV cache reuse↑ PrefillKV-aware routerDecode 02 · Engine interoperability Bring your inference engineUse Dynamo with vLLM, SGLang, or TensorRT-LLM, then connect the network, storage, and KV cache layers you need. Dynamo Serve $ dynamo serve --backend vllm
✓ frontend ready
✓ router connected
✓ workers discovered
Serving on :8000 vLLMSGLangTensorRT-LLM 03 · Infrastructure choice Run where your workloads runDeploy on Kubernetes, schedule with Slurm, or start locally across NVIDIA and AMD GPUs and Intel XPUs. Dynamo KubernetesSlurmLocalNVIDIA GPUAMD GPUIntel XPU 04 · Modular adoption Adopt one component or the full stackStart with the frontend, router, planner, or cache manager, and add the rest as your deployment grows. FrontendKV routerPlannerKV cache managerWorkers Start smallScale out Dynamo Metrics Time to first token↓ Request throughput↑ KV cache reuse↑ PrefillKV-aware routerDecode Dynamo Serve $ dynamo serve --backend vllm
✓ frontend ready
✓ router connected
✓ workers discovered
Serving on :8000 vLLMSGLangTensorRT-LLM Dynamo KubernetesSlurmLocalNVIDIA GPUAMD GPUIntel XPU FrontendKV routerPlannerKV cache managerWorkers Start smallScale out
Meetups, talks, and community meetings Community eventsPreviewed from the public Dynamo Google Calendar.Nov12 Upcoming event Thu, Nov 12, 2026 Nov12 Upcoming event Thu, Nov 12, 2026 Nov17 +Nov19 Upcoming event Thu, Nov 19, 2026 Recent eventsThu, Nov 5, 2026Dynamo SIG RLThu, Nov 5, 2026SIG RouterTue, Nov 3, 2026Dynamo SIG DeployThu, Oct 15, 2026CoreWeave x NVIDIA Dynamo MeetupTue, Oct 13, 2026Dynamo Engineering Sync |