What is ONNX Runtime?
ONNX Runtime is cross-platform inference engine supporting ONNX model format with optimizations for diverse hardware. ONNX Runtime enables portable, optimized inference across CPUs, GPUs, and accelerators.
This model optimization and inference term is currently being developed. Detailed content covering implementation approaches, performance tradeoffs, best practices, and deployment considerations will be added soon. For immediate guidance on model optimization strategies, contact Pertama Partners for advisory services.
ONNX Runtime provides hardware-agnostic model deployment that prevents vendor lock-in to specific AI frameworks, enabling mid-market companies to switch between TensorFlow and PyTorch models without rewriting serving infrastructure. Companies standardizing on ONNX format reduce deployment engineering effort by 50-70% since a single serving pipeline handles models from any training framework consistently. The built-in optimization passes deliver 20-40% inference speedups over naive deployment, translating directly into lower compute costs and faster response times for production AI services.
- Cross-platform: Windows, Linux, macOS, mobile.
- Hardware: CPU, CUDA, DirectML, TensorRT, OpenVINO.
- Framework agnostic (ONNX interchange format).
- Graph optimizations and kernel fusion.
- Lower-level than framework inference.
- Good for production deployment portability.
- Export models to ONNX format early in development to validate cross-platform compatibility before investing in deployment infrastructure tied to specific hardware configurations.
- Enable ONNX Runtime graph optimizations including operator fusion and constant folding to achieve 20-40% inference speedups without any model architecture modifications required.
- Use execution provider selection to automatically route inference to the fastest available hardware, supporting seamless transitions between CPU, GPU, and specialized accelerator deployments.
- Benchmark ONNX Runtime performance against native framework inference on your production workload, since conversion overhead occasionally produces slower execution on certain model architectures.
Common Questions
When should we quantize models?
Quantize for deployment when inference cost or latency is concern and minor quality degradation is acceptable. Test quantized models thoroughly on your use cases. 8-bit quantization typically has minimal impact, 4-bit requires more careful evaluation.
How do we choose inference framework?
Consider model format compatibility, hardware support, performance requirements, and operational preferences. vLLM excels for high-throughput serving, TensorRT-LLM for low latency, Ollama for local deployment simplicity.
More Questions
Batching increases throughput but raises per-request latency. Optimize for throughput in offline batch processing, latency for interactive applications. Continuous batching balances both for variable workloads.
References
- NIST Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology (NIST) (2023). View source
- Stanford HAI AI Index Report 2025. Stanford Institute for Human-Centered AI (2025). View source
Inference in AI is the process of running a trained model to generate outputs -- such as predictions, text responses, image classifications, or recommendations -- from new input data. It is the production phase of AI where the model delivers value to end users, as opposed to the training phase where the model learns.
Inference is the process of using a trained AI model to make predictions or decisions on new, unseen data in real time, representing the production phase where AI delivers actual business value by processing customer requests, analysing images, generating text, or making recommendations.
Repetition Penalty reduces probability of previously generated tokens to discourage repetitive text, improving output diversity. Repetition penalties are essential for coherent long-form generation.
Stop Sequences are tokens or strings that trigger generation termination when encountered, enabling control over output length and format. Stop sequences are critical for structured generation and chat applications.
Structured Generation constrains model outputs to match specified formats (JSON, XML, grammars) through constrained decoding. Structured generation ensures parseable, valid outputs for integration with systems.
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