BMInf, short for Big Model Inference, is the OpenBMB community’s open-source toolkit for running very large pre-trained language models efficiently on limited GPU hardware. It exists for a familiar problem: the most interesting models are too big for the graphics card you actually own. The project is released under the Apache-2.0 license, has about 587 stars on GitHub, and was updated in July 2026.
BMInf applies memory- and compute-saving techniques so that large transformer models can perform inference on modest hardware, combining GPU computation with offloading strategies rather than demanding a data-center card. Since version 2.0 the toolkit applies to transformer-based models generally, not only to the CPM models from the same community that it originally targeted. It is built on PyTorch, supports backpropagation, and exposes a straightforward generate-style interface for text generation, which keeps experiments close to ordinary Hugging Face workflows. Documentation is hosted on Read the Docs, and the project grew out of the OpenBMB ecosystem around large Chinese and multilingual models, where making big models runnable outside big labs is a core goal.
The toolkit grew out of work on the CPM series of large models, and its techniques, including offloading parts of a model between GPU and CPU memory, are aimed at a single workstation rather than a cluster. That focus keeps it relevant for university labs and independent researchers with one good graphics card.
BMInf is free and open source under the Apache-2.0 license. There is no paid edition; you run it on your own hardware with your own models.
BMInf is for researchers, students and independent developers who want to run or fine-tune large language models without access to high-end GPU servers.