NVIDIA Triton Inference Server
NVIDIA Triton Inference Server, part of Dynamo-Triton, is open-source software for deploying and running AI models on GPU- or CPU-based infrastructure.
What NVIDIA Triton Inference Server is
NVIDIA Triton Inference Server, part of Dynamo-Triton, is open-source software for deploying and running AI models on GPU- or CPU-based infrastructure. It supports TensorRT, PyTorch, ONNX, OpenVINO, Python, and RAPIDS FIL, with formats including TensorRT Plan, ONNX, TensorFlow GraphDef and SavedModel, PyTorch TorchScript, and PyTorch 2.0. Triton serves real-time, batched, ensemble, and audio/video streaming workloads, with dynamic batching and concurrent model execution. Requests can use HTTP/REST, gRPC, or the C API, and a Java API supports in-process use. It integrates with Kubernetes for scaling and Prometheus for monitoring, and runs on NVIDIA GPUs, other accelerators, and x86 or ARM CPUs across cloud, data center, edge, and embedded deployments. Open-source code and free NGC containers support development. Production options include NVIDIA AI Enterprise, listed at $4,500 a year per GPU for a subscription; cloud marketplace use is pay as you go. NVIDIA says deployers are responsible for security and Triton does not sandbox arbitrary model or backend code.
Good
- Open-source development option
- Supports multiple model frameworks and formats
- Handles real-time and batched workloads
- Integrates with Kubernetes and Prometheus
- Runs on GPU and CPU infrastructure
Watch out
- Security is the deployer's responsibility
- Does not sandbox arbitrary model or backend code
- AI Enterprise subscription costs $4,500 a year per GPU
- Cloud marketplace production support is limited to 3 calls
Who it suits
Triton suits developers deploying inference workloads across supported model frameworks and GPU or CPU infrastructure. NVIDIA positions GitHub and NGC options for individual development and AI Enterprise for enterprise production use.
Verdict
Triton offers broad deployment and workload support, with open-source development and separate enterprise production options. Production teams should account for the listed licensing choices and take responsibility for securing deployments.
Plans and prices
From NVIDIA Triton Inference Server's own pricing page, checked 4 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| Open-source development | Free | Open-source code on GitHub · free Triton containers on NVIDIA NGC for development |
| NVIDIA AI Enterprise cloud production | Priced on request | Cloud marketplace production use · support limited to 3 calls · Consumption / Pay as you go |
| NVIDIA AI Enterprise subscription | $4,500 a year | Per GPU · for production use · Business Standard Support included · 1 year; subscription includes support |
The facts
Each one read from NVIDIA Triton Inference Server's own pages, with the date it was checked.
Where it runs
- Deployment platforms
- It runs on NVIDIA GPUs, non-NVIDIA accelerators, x86 and ARM CPUs, and supports cloud, data center, edge, and embedded deployments.checked 4 Oct 2026
What it does
- Downloads
- NVIDIA lists Linux containers for x86 and Arm, plus Windows and Jetson JetPack binary releases on GitHub.checked 4 Oct 2026
- Enterprise support
- NVIDIA AI Enterprise includes Business Standard Support with 24/7 case filing, local business-hours support coverage, a four-hour initial response, and up to three years of long-term branch maintenance for selected components.checked 4 Oct 2026
- Evaluation
- NVIDIA offers a 90-day NVIDIA AI Enterprise evaluation license for Triton production inference.checked 4 Oct 2026
- Frameworks
- Supported frameworks include TensorRT, PyTorch, ONNX, OpenVINO, Python, and RAPIDS FIL.checked 4 Oct 2026
- Integrations
- It integrates with Kubernetes for scaling and Prometheus for monitoring, and NVIDIA lists availability through AWS, Azure, and Google Cloud marketplaces with NVIDIA AI Enterprise.checked 4 Oct 2026
- Performance features
- It offers dynamic batching, concurrent model execution, and optimized configurations.checked 4 Oct 2026
- Protocols
- Inference requests can use HTTP/REST, gRPC, or the C API; Triton also provides a Java API for in-process use cases.checked 4 Oct 2026
- Purpose
- Dynamo-Triton is open-source inference-serving software for deploying, running, and scaling AI models from multiple frameworks on GPU- or CPU-based infrastructure.checked 4 Oct 2026
- Security
- NVIDIA's secure-deployment guide says solution security is the deployer's responsibility, dynamic model repository updates are disabled by default, and Triton does not sandbox arbitrary model or backend code.checked 4 Oct 2026
- Who it is for
- NVIDIA describes GitHub and NGC options for individuals developing with Triton and NVIDIA AI Enterprise for enterprises purchasing it for production.checked 4 Oct 2026
- Workloads
- It supports real-time, batched, ensemble, and audio/video streaming inference workloads.checked 4 Oct 2026
The company
- Founded
- 1993checked 28 Sep 2026
- Headquarters
- Santa Clara, California, United Stateschecked 28 Sep 2026
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