Optimum is a hardware optimization toolkit that accelerates inference and training of Transformers, Diffusers, TIMM, and Sentence Transformers models.
The tool addresses the challenge of deploying machine learning models efficiently on diverse hardware platforms. Rather than requiring users to learn platform-specific optimization techniques, Optimum provides a unified interface that abstracts away hardware complexity. It enables model optimization through quantization, graph compilation, and format conversion while maintaining compatibility with the original model APIs.
Optimum suits teams deploying models to specific accelerators or inference engines where performance matters. The project supports a broad range of hardware targets including ONNX Runtime, OpenVINO, NVIDIA TensorRT-LLM, AMD GPUs, AWS Trainum and Inferentia, Intel Gaudi accelerators, and FuriosaAI. This breadth means you can optimize once and target multiple platforms, or focus on a single accelerator without learning its low-level APIs. The tool integrates directly with popular model libraries, so optimization workflows feel natural to users already familiar with Transformers and related frameworks.
The project maintains active development across multiple optimization backends, with dedicated documentation and installation paths for each hardware target. Contributors regularly expand accelerator support and refine optimization techniques across different model architectures. The codebase shows sustained effort in keeping pace with both hardware evolution and advances in model optimization research.
