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Optimum Transformers

Optimum Transformers is a Python library that runs Hugging Face-style NLP pipelines through ONNX Runtime for CPU or GPU inference, with an option for standard Torch inference.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Optimum Transformers — OpenViST project profile in AI Infrastructure & Serving.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want a Transformers-like pipeline interface for ONNX-based NLP inference, or when comparing inference performance on your own CPU or GPU hardware.

Who it is for

  • Python developers adding NLP inference to applications or services.
  • Practitioners evaluating ONNX-based inference performance on their own hardware.

Documented uses

  • Run sentiment analysis, question answering, NER, classification, text generation, or other listed pipeline tasks.
  • Choose a model by task or provide a specific model.
  • Enable ONNX quantization or use standard Torch inference.
  • Benchmark pipeline performance on target hardware.
Before you choose

Requirements & limits

The project is open source under the GPL-3.0 license and documents installation through PyPI or directly from GitHub.

  • ONNX is enabled by default in the documented pipeline example; set use_onnx=False for standard Torch inference.
  • optimize=True is described as enabling ONNX quantization and requires ONNX to be enabled.
  • The first call creates and caches the ONNX graph, making first-load time longer than subsequent calls.
  • The repository warns that Colab may not show ONNX speed-ups and advises benchmarking on the target hardware.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Run a sentiment-analysis pipeline with ONNX

Create a pipeline for sentiment analysis and run it on input text.

Before you start

  • Python environment with the package installed using PyPI or the GitHub installation command.
  • A model available locally or from the Hugging Face Hub; the library can download a model if needed.

Steps

  1. Install the package with the command below.

    Documented command
    pip install optimum-transformers

  2. Import pipeline from optimum_transformers and create it with pipeline("sentiment-analysis", use_onnx=True).

  3. Call the pipeline with a text string to receive a sentiment label and score.

Expected outcome

A sentiment-analysis result is returned for the input text.

Guide limitations

  • The first pipeline call loads the model and creates the ONNX graph, so it takes longer; later calls can reuse the cached graph.
  • The project cautions that ONNX may be slow in Colab and recommends benchmarking on the intended hardware.
Documentation 1 ↗
Source-based practical guide

Benchmark an NLP pipeline on your own hardware

Use the repository's example to assess inference performance on the hardware you intend to use.

Before you start

  • An installed project environment and hardware on which to benchmark.
  • The repository's example benchmarking files.

Steps

  1. Open the repository's examples/benchmark example.

  2. Run the documented benchmark for the NLP pipeline and hardware you want to assess.

  3. Compare the observed performance on that hardware rather than relying on the repository's local-machine results.

Expected outcome

A hardware-specific performance measurement for the selected NLP pipeline.

Guide limitations

  • The README does not provide the benchmark commands in the supplied documentation.
  • The project notes that ONNX may be slow in Colab; results vary with hardware.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
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Sources checked
17 October 2026
Original website title
AlekseyKorshuk/optimum-transformers · Official homepage title · retained in its source language
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