optimum
Optimum Library is an extension of the Hugging Face Transformers library, providing a framework to integrate third-party libraries from Hardware Partners and interface with their specific functionality.
Decision gist · record as of 2026-08-14
Yes. Optimum is actively maintained, has no known vulnerabilities, and solves a real problem for anyone deploying HuggingFace models on non-standard hardware or needing quantization and export. The permissive Apache license and low install friction make it a low-risk addition. Install the base package for export capabilities, then add hardware-specific extras only when needed.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later.
- Hardware-specific features (ONNX Runtime, OpenVINO, Trainium, etc.) require additional optional dependencies installed separately.
- Low install friction with a pure-wheel distribution.
License · maintenance · safety
Apache (permissive) — Licensed under Apache 2.0 (permissive), allowing free use, modification, and distribution with minimal restrictions—suitable for both open-source and commercial projects.
last release 2026-08-04 (10 days) · last repo commit 2026-08-10 · 3,461 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,055,714 downloads/mo, #3,336 on PyPI
Alternatives
Verify before relying
pip install optimum
from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoTokenizer
model = ORTModelForSequenceClassification.from_pretrained(
"model_id", from_transformers=True
)
tokenizer = AutoTokenizer.from_pretrained("model_id")
outputs = model(**tokenizer("Hello world", return_tensors="pt"))- Performance gains and latency improvements for specific hardware targets and model sizes
- Compatibility matrix and tested model architectures beyond Transformers
- Memory overhead during export and optimization phases
What it is and what it does
Optimum is a framework that bridges HuggingFace models and specialized hardware accelerators, enabling efficient inference and training. It wraps Transformers, Diffusers, TIMM, and Sentence-Transformers to provide a unified interface for exporting models to optimized formats (ONNX, OpenVINO, ExecuTorch) and running them on diverse hardware—from Intel Gaudi HPUs and AWS Trainium to NVIDIA GPUs and edge devices. The core library handles the export logic and provides wrapper classes; hardware-specific optimizations are installed as optional extras.
Developers use Optimum when they need to deploy models beyond standard PyTorch inference—whether for quantization, pruning, cross-platform compatibility, or leveraging specialized accelerators. It abstracts away low-level hardware details while keeping the HuggingFace API familiar, so you can export a Transformers model and run it on Intel hardware or AWS instances without rewriting your inference code.
Use it for
- Export Transformers models to ONNX format for cross-platform deployment and graph optimization
- Run quantized models on edge devices using ExecuTorch or OpenVINO for reduced latency and memory
- Accelerate training on AWS Trainium or Intel Gaudi HPUs with minimal code changes to the standard Trainer
- Deploy optimized models on NVIDIA GPUs via ONNX Runtime with performance tuning
- Integrate third-party hardware partner libraries (Intel, AWS, AMD) without managing separate APIs
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Optimum is actively maintained, has no known vulnerabilities, and solves a real problem for anyone deploying HuggingFace models on non-standard hardware or needing quantization and export. The permissive Apache license and low install friction make it a low-risk addition. Install the base package for export capabilities, then add hardware-specific extras only when needed.
Install
optimum on PyPI
Before you install
Low install friction with a pure-wheel distribution. Active maintenance with a recent release (10 days old) and steady repository activity. Depends on core libraries like transformers, torch, and huggingface_hub, which are standard in the ML ecosystem.
Requires Python 3.9 or later. Hardware-specific features (ONNX Runtime, OpenVINO, Trainium, etc.) require additional optional dependencies installed separately.
License in practice
Licensed under Apache 2.0 (permissive), allowing free use, modification, and distribution with minimal restrictions—suitable for both open-source and commercial projects.
Quickstart
pip install optimum
from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoTokenizer
model = ORTModelForSequenceClassification.from_pretrained(
"model_id", from_transformers=True
)
tokenizer = AutoTokenizer.from_pretrained("model_id")
outputs = model(**tokenizer("Hello world", return_tensors="pt"))Verify before relying
- Performance gains and latency improvements for specific hardware targets and model sizes
- Compatibility matrix and tested model architectures beyond Transformers
- Memory overhead during export and optimization phases
Package facts
| License | Apache permissive |
| Python support | Supports the current Python release >=3.9.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagestransformerstorchpackagingnumpyhuggingface_hub |
| Maintenance | Actively maintained 10 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 2,055,714 / month, #3,336 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: optimum-2.3.0-py3-none-any.whl
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See also optimum-intel · optimum-onnx · optimum-quanto · onnxslim · onnxruntime-openvino · nvidia-modelopt · executorch · openvino · onnxruntime-genai · onnxruntime-gpu