Side by side
NVIDIA TensorRT vs NVIDIA Triton Inference Server
Both stand on deep learning software. Which one you can start free with, which one lets you read the code, and what each costs once you pay.
Choose NVIDIA TensorRT if you need a free development toolkit to optimize deep learning inference on supported NVIDIA GPUs, with integrations for common model workflows and Triton serving. Look elsewhere if you need serialized engines to move between operating systems or require an SDK NVIDIA has tested and certified for critical applications.
Visit NVIDIA TensorRTTriton provides broad framework, protocol, hardware, and deployment coverage for model serving. Production users should account for the separate NVIDIA AI Enterprise options and the stated security responsibilities.
Visit NVIDIA Triton Inference ServerThe facts
| NVIDIA TensorRT | NVIDIA Triton Inference Server | |
|---|---|---|
| Free plan | Yes | Yes |
| Open source | Not published | Not published |
| Runs on | Linux, Self-hosted, Windows | API, Linux, Self-hosted, Windows |
| Lowest paid plan | Free or on request | NVIDIA AI Enterprise subscription: $4,500 a year |
| Plans published | 2 | 3 |
| Maker | Not published | Not published |
| Website | developer.nvidia.com | developer.nvidia.com |
| On deep learning software | #21 of 35 | #17 of 35 |
NVIDIA TensorRT: what it does well
- Free development SDK with binary and NVIDIA NGC container downloads.
- Optimization includes quantization, layer and tensor fusion, and kernel tuning.
- TensorRT Model Optimizer supports FP8, FP4, INT8, INT4, and AWQ.
- Integrates with PyTorch and Hugging Face and imports ONNX models.
- Triton backend supports dynamic batching and concurrent model execution.
What to know first
- Requires NVIDIA hardware with compute capability SM 7.5 or higher.
- Serialized engines are not portable between platforms such as Linux and Windows.
- TensorRT 10.0 GA download requires NVIDIA Developer Program membership.
- NVIDIA says it has not tested or certified the SDK for critical applications.
NVIDIA Triton Inference Server: what it does well
- Open-source code and free development containers
- Supports multiple model frameworks and formats
- Handles real-time, batch, ensemble, and streaming workloads
- HTTP, gRPC, C, and Java APIs
- Runs across GPUs, CPUs, cloud, edge, and embedded systems
What to know first
- Security is the deployer's responsibility
- Arbitrary model or backend code is not sandboxed
- Production subscription listed at $4,500 a year per GPU
- Cloud production support limited to three calls
Alternatives to NVIDIA TensorRT Alternatives to NVIDIA Triton Inference Server The whole list