ONNX Runtime Inference
ONNX Runtime Inference is the practice of running pretrained models efficiently on diverse hardware. Includes Python/C++/JavaScript APIs, hardware acceleration (GPU, TensorRT, OpenVINO), batching, memory management, and monitoring. Used by ML engineers, DevOps, and production teams. Practitioners earn 30-40% premium for inference optimization. Time to mastery: 10-14 weeks. Sits between ONNX format and production deployment.
What is ONNX Runtime Inference
ONNX Runtime is Microsoft's open-source inference engine for running ONNX models efficiently on any hardware: CPUs, GPUs (CUDA/TensorRT), mobile (iOS/Android), web (WebAssembly), and edge devices. It provides language bindings (Python, C++, C#, JavaScript, Java, Go, Rust), optimization passes, hardware acceleration, and performance monitoring. Inference (running pretrained models) is production's bottleneck. ONNX Runtime optimizes latency, throughput, and memory usage. A well-tuned inference pipeline can serve 10x more requests on same hardware.
📋 Before you start
💰 Salary by region
| Region | Junior | Mid | Senior |
|---|---|---|---|
| USA | $90k | $150k | $240k |
| UK | $55k | $95k | $150k |
| EU | $60k | $105k | $160k |
| CANADA | $95k | $155k | $250k |
🎯 Careers using ONNX Runtime Inference
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❓ FAQ
What's the difference between ONNX Runtime and TensorRT?
How do I batch requests for efficiency?
Should I use GPU or CPU?
How do I handle model updates without downtime?
What about quantized model inference?
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