DeepSpeed vs NVIDIA Triton Inference Server
DeepSpeed
deepspeed.aiNVIDIA Triton Inference Server
developer.nvidia.comSide by side
| Figure | DeepSpeed | NVIDIA Triton Inference Server |
|---|---|---|
| Price from | Free | $375/mo |
| Free plan | Yes | Yes |
| Free trial | — | Yes |
| Open source | — | — |
| Windows | — | Yes |
| macOS | Yes | — |
| Linux | Yes | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 3 |
| Spec score · Deep Learning Software | 8.0 (#12) | 8.0 (#13) |
| Integrations | The site lists integrations with Hugging Face Transformers, Accelerate, PyTorch Lightning and MosaicML. | 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. |
| Purpose | DeepSpeed is a deep learning optimization library for distributed model training and inference. | Dynamo-Triton is open-source inference-serving software for deploying, running, and scaling AI models from multiple frameworks on GPU- or CPU-based infrastructure. |
| Security | The repository links to a SECURITY file and identifies the project as Apache-2.0 licensed. | 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. |
Plans
NVIDIA Triton Inference Server
- Open-source developmentFree
- NVIDIA AI Enterprise cloud productionPriced on request
- NVIDIA AI Enterprise subscription$4,500 a year
In their own words
NVIDIA Triton Inference Server
NVIDIA Triton Inference Server is open-source software for deploying and serving AI models on GPU- or CPU-based infrastructure. It supports TensorRT, PyTorch, ONNX, OpenVINO, Python and RAPIDS FIL, with model 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. Dynamic batching and concurrent model execution are among its performance features. Requests can use HTTP/REST, gRPC or the C API, with a Java API for in-process use. Triton integrates with Kubernetes for scaling and Prometheus for monitoring. Deployment options include NVIDIA GPUs, other accelerators, x86 and ARM CPUs, cloud, data centers, edge and embedded systems. NVIDIA lists Linux containers for x86 and Arm, plus Windows and Jetson JetPack binaries. Development code and containers are free; production options include a cloud marketplace plan billed pay as you go and a subscription listed at $4,500 a year per GPU. NVIDIA says deployers are responsible for security, and Triton does not sandbox arbitrary model or backend code.