NVIDIA Dynamo vs Dynamo-Triton
Dynamo coordinates LLM inference across many GPUs and nodes on top of engines such as vLLM, SGLang and TensorRT LLM. Dynamo-Triton, formerly Triton Inference Server, serves models from many frameworks. NVIDIA calls Dynamo both Triton's successor and a complement to it.12
How they relate
Complementary. They do different jobs and are often used together.
The two share a brand but solve different problems. Dynamo-Triton is the current name of Triton Inference Server, a general model server that loads models from frameworks such as TensorRT, PyTorch, ONNX, OpenVINO, Python and RAPIDS FIL and runs on GPUs and CPUs. Dynamo is an open source framework for distributed inference of generative models: it sits above engines such as vLLM, SGLang and TensorRT LLM and adds disaggregated prefill and decode, KV-cache-aware routing and cache offload to cheaper storage tiers.
NVIDIA's own pages describe the relationship in two ways. The Dynamo developer page calls Dynamo the successor to Triton Inference Server and says it builds on Triton's earlier work. The Dynamo-Triton developer page says Dynamo complements Dynamo-Triton with LLM-specific optimizations. In our reading both hold at once: for distributed LLM serving, Dynamo is where NVIDIA's new work goes, while Dynamo-Triton remains the general server for many model types.
They can run side by side in one organization, for example Dynamo for a large chat model and Dynamo-Triton for vision or tabular models. There is no universal winner. Support also differs today: NVIDIA says AI Enterprise includes Triton Inference Server, while its Dynamo developer page says AI Enterprise will include Dynamo in a future release.123
The technologies
Framework
NVIDIA Dynamo
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.
Product
NVIDIA Dynamo-Triton
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.
Which fits which goal
| If your goal is | What fits | Why |
|---|---|---|
| Serve computer vision, tabular or mixed-framework models | Dynamo-Triton | It deploys models from TensorRT, PyTorch, ONNX, OpenVINO, Python and RAPIDS FIL, and its documentation lists cloud, data center, edge and embedded targets on GPUs, CPUs or AWS Inferentia.4 |
| Serve one large LLM across many GPUs or nodes against latency targets | Dynamo, with vLLM, SGLang or TensorRT LLM as the engine | Dynamo adds disaggregated prefill and decode, a KV-cache-aware router, a KV block manager and an SLO planner that adjusts GPU resources, and it works with those three engines.1 |
| Run one model on one GPU | None of these: the inference engine on its own, or Dynamo-Triton only if you want a standard server around it | The Dynamo README says a single model on a single GPU is probably served well enough by the inference engine alone.3 |
| Production support under an NVIDIA AI Enterprise license today | Dynamo-Triton | NVIDIA says AI Enterprise includes Triton Inference Server for production inference, while it says Dynamo will join AI Enterprise in a future release.12 |
No technology here is ranked. Pricing and performance are left out on purpose: they depend on your models, hardware and contract, so measure them in a pilot.
Sources
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