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Compare NVIDIA technologies
Pick up to four. The comparison starts by saying whether they compete, work together or sit in different layers, then lines up their verified facts. It never names a universal winner.
How they relate
NVIDIA Dynamo and NVIDIA Dynamo-Triton work together.
From the published profiles: same product family.
Side by side
| Fact | NVIDIA Dynamo | NVIDIA Dynamo-Triton |
|---|---|---|
| Type | Framework | Product |
| Layers | Inference & runtime software, Operations & orchestration | Inference & runtime software |
| What it is | NVIDIA Dynamo is an open source framework that coordinates generative AI inference across many GPUs and nodes. It sits above engines such as vLLM, SGLang and TensorRT LLM and adds disaggregated serving, KV-cache-aware routing, cache offload and latency-driven autoscaling. | NVIDIA Dynamo-Triton, formerly Triton Inference Server, is open source inference serving software that runs models from many frameworks, including TensorRT, PyTorch, ONNX, OpenVINO, Python and RAPIDS FIL, on GPUs and CPUs behind HTTP/REST and gRPC APIs. |
| Who needs it | Teams serving large language or reasoning models across several GPUs or nodes, where throughput, time to first token and GPU cost matter: cloud providers, AI factory operators and platform teams running shared inference on Kubernetes or Slurm. The Dynamo README states that a single model on a single GPU is probably served well enough by the inference engine alone. | Teams that serve many kinds of models (vision, speech, recommendation, tree-based and language models) from one consistent server; MLOps teams that need Kubernetes scaling and Prometheus monitoring; and edge or embedded projects on Jetson or Windows that want the same serving software as the data center. |
| What it is not | Dynamo is not an inference engine and does not compete with TensorRT LLM, vLLM or SGLang; it orchestrates them. It is not the same as NIM, which packages a model and engine as a container, although NVIDIA says NIM will include Dynamo capabilities and the NIM Operator can deploy Dynamo resources experimentally. Dynamo is not part of NVIDIA AI Enterprise today: NVIDIA's Dynamo page says AI Enterprise will include it in a future release. It is not limited to NVIDIA hardware either; its documentation lists NVIDIA and AMD GPUs and Intel XPUs. | Dynamo-Triton is not the same as Dynamo. NVIDIA positions Dynamo for distributed LLM inference with disaggregated serving and KV-cache features and says it complements Dynamo-Triton; the Dynamo developer page also calls Dynamo the successor to Triton. Dynamo-Triton is not an engine like TensorRT LLM, which it can run through a backend, and not a packaged model container like NIM. After the rename, images, repositories and commands still use the Triton name. |
| Prerequisites |
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| Runs on | Kubernetes (Dynamo Platform and Grove), Slurm clusters, Local single node, AWS EKS, Google GKE, Azure AKS and Amazon ECS, NVIDIA GPUs, AMD GPUs and Intel XPUs | Public cloud, On-premises data center, Edge and embedded (Jetson), Windows, Kubernetes, CPU-only servers |
| Availability and licensing | Dynamo is open source on GitHub (the repository shows an Apache 2.0 license badge), with prebuilt runtime containers on NGC and packages on PyPI. NVIDIA's Dynamo page states that NVIDIA AI Enterprise will include Dynamo for production inference in a future release, so it is not part of AI Enterprise today. | Dynamo-Triton is open source; the server repository on GitHub is licensed BSD-3-Clause, and x86 and Arm containers are available on NGC; the repository also ships an NVIDIA Deep Learning Container License file. NVIDIA offers enterprise support through NVIDIA AI Enterprise, which includes Triton Inference Server for production, with a 90-day evaluation license. |
| When something simpler is enough | If you run one model on one GPU, the Dynamo README itself says the inference engine alone is probably enough. A managed model API avoids running GPU clusters entirely. Teams that want a packaged, supported container rather than a framework to assemble may prefer NIM. For computer vision, tabular or other non-LLM models from many frameworks, Dynamo-Triton is the more general server. | If you serve a single model from one framework at low volume, that framework's own serving option or a small web service may be enough. For large LLMs spread across many GPUs, NVIDIA points to Dynamo, which adds LLM-specific features such as disaggregated serving and KV caching to storage. If you need a supported container for one popular generative model with an OpenAI-style API, a NIM may need less setup. |
| Last reviewed | 9 Oct 2026 | 9 Oct 2026 |
| Official resources | Product page (opens in a new tab) Documentation (opens in a new tab) | Product page (opens in a new tab) Documentation (opens in a new tab) |
Each value comes from the technology's profile, where every statement links to its source. Pricing, benchmark and compatibility claims are not shown unless a profile documents them.
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