Migrate From Trtiton

NVIDIA Triton Inference Server (Triton) is an inference serving system from NVIDIA. Open Edge Platform’s alternative for deploying production-grade AI inference service is OpenVINO Model Server (OVMS)

Comparison Summary

Feature	NVIDIA Triton	Intel OVMS
Best hardware	NVIDIA GPUs	Intel CPUs, iGPUs, GPUs, NPUs, VPUs
Core engine	TensorRT, CUDA	OpenVINO toolkit
Frameworks supported	TensorFlow, PyTorch, ONNX, TensorRT, etc.	TensorFlow, ONNX (via OpenVINO)
Interfaces	HTTP/gRPC	HTTP/gRPC (TensorFlow Serving API)
Batching & optimization	Yes (dynamic, GPU-accelerated)	Yes (CPU/GPU optimized)
Metrics & observability	Prometheus, logs	Prometheus, logs
Containerized deployments	Yes (NGC)	Yes (DockerHub)

If you want your serving to benefit from Intel architecture, you have several options:

Deploy models on Triton Inference Server using the OpenVINO backend.

Check out the NVIDIA guide on how to use Triton with the OpenVINO backend

Migrate from Triton to OVMS

To start, make sure you use model formats supported by OVMS.

If not, you will need to convert them first. Once the models are

ready, organize your model repository

to reflect the Triton’s repository layout.

Configure each model with input/output definitions and versioning as required and

deploy OVMS.

By loading the repository and exposing the REST and gRPC endpoints, you will make the

models available to your existing pipeline. Because OVMS supports Triton’s client APIs,

the existing clients should work without modification. However, features like shared

memory interfaces or custom backends may need adjustments. To ensure proper quality,

perform a comparative test for the new solution’s accuracy and performance metrics.