From Build to Benchmark: ONNX Model Serving with Triton Inference Server on AMD GPUs

2026-05-22 · Source: AMD ROCm Blogs · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Software Development & Engineering · Depth: Intermediate, long

Summary

AMD provides a comprehensive guide for deploying and serving ONNX models on its Instinct MI300X GPUs using Triton Inference Server with the ONNX Runtime backend. This process, detailed for Ubuntu 24.04 and Debian 12, involves building a Triton Inference Server Docker image with ROCm 7.2+ and MIGraphX support. The guide outlines setting up a model repository for the ResNet50-v2 model, configuring its "config.pbtxt" to utilize MIGraphX for optimized execution and dynamic batching, and launching the server. It further demonstrates sending inference requests via a Python client and measuring performance metrics like throughput and latency using "perf_analyzer". This approach fully leverages AMD's hardware and software stack for high-performance ONNX model inference.

Key takeaway

For MLOps Engineers deploying ONNX models on AMD Instinct GPUs, this guide offers a robust, step-by-step methodology to achieve high-throughput, low-latency inference. You should follow the detailed instructions for building the Triton Inference Server Docker image with ROCm and ONNX Runtime. Configure MIGraphX for graph optimization and leverage dynamic batching. This ensures your models fully utilize AMD hardware capabilities, and you can validate performance using "perf_analyzer".

Key insights

Triton Inference Server with ONNX Runtime and MIGraphX optimizes ONNX model serving on AMD GPUs.

Principles

• MIGraphX optimizes ONNX graphs for AMD GPUs.
• Model caching speeds up server restarts.
• Dynamic batching enhances GPU utilization.

Method

Build Triton Inference Server Docker image with ROCm and ONNX Runtime, configure model repository with MIGraphX, run server with GPU access, then perform inference and benchmark.

In practice

• Enable MIGraphX and dynamic batching in "config.pbtxt".
• Cache compiled models using "ORT_MIGRAPHX_MODEL_CACHE_PATH".
• Benchmark with "perf_analyzer" to find bottlenecks.

Topics

• Triton Inference Server
• ONNX Runtime
• AMD GPUs
• ROCm
• MIGraphX
• Model Serving
• Performance Benchmarking

Code references

• triton-inference-server/backend
• ROCm/AMDMIGraphX
• onnx/models
• ROCm/triton-inference-server-server
• ROCm/triton-inference-server-core

Best for: MLOps Engineer, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by AMD ROCm Blogs.