{"data":{"slug":"physical-intelligence-openpi","name":"openpi","tagline":"Repository for running AI models with GPU requirements specified.","github_url":"https://github.com/Physical-Intelligence/openpi","owner":"Physical-Intelligence","repo":"openpi","owner_avatar_url":"https://avatars.githubusercontent.com/u/162759805?v=4","primary_language":"Python","stars":12742,"forks":2187,"topics":[],"archived":false,"github_pushed_at":"2026-06-16T00:14:01+00:00","maintenance_label":"Active","url":"https://www.graphcanon.com/tools/physical-intelligence-openpi","markdown_url":"https://www.graphcanon.com/tools/physical-intelligence-openpi.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/physical-intelligence-openpi","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=physical-intelligence-openpi","description":null,"homepage_url":null,"license":"Apache-2.0","open_issues":312,"watchers":89,"ai_summary":"A collection of AI models that necessitate specific NVIDIA GPU configurations for inference and fine-tuning, including options for model parallelism. The setup is primarily tested under Ubuntu 22.04.","readme_excerpt":"## Requirements\n\nTo run the models in this repository, you will need an NVIDIA GPU with at least the following specifications. These estimations assume a single GPU, but you can also use multiple GPUs with model parallelism to reduce per-GPU memory requirements by configuring `fsdp_devices` in the training config. Please also note that the current training script does not yet support multi-node training.\n\n| Mode               | Memory Required | Example GPU        |\n| ------------------ | --------------- | ------------------ |\n| Inference          | > 8 GB          | RTX 4090           |\n| Fine-Tuning (LoRA) | > 22.5 GB       | RTX 4090           |\n| Fine-Tuning (Full) | > 70 GB         | A100 (80GB) / H100 |\n\nThe repo has been tested with Ubuntu 22.04, we do not currently support other operating systems.\n\n---\n\n## Installation\n\nWhen cloning this repo, make sure to update submodules:\n\n```bash\ngit clone --recurse-submodules git@github.com:Physical-Intelligence/openpi.git","github_created_at":"2024-10-21T15:23:28+00:00","created_at":"2026-07-11T23:10:04.061015+00:00","updated_at":"2026-07-12T03:39:55.858832+00:00","categories":[{"slug":"inference-serving","name":"Inference & Serving","url":"https://www.graphcanon.com/categories/inference-serving","markdown_url":"https://www.graphcanon.com/categories/inference-serving.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/inference-serving"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"fine-tuning","name":"fine-tuning"},{"slug":"lora","name":"lora"},{"slug":"model-parallelism","name":"model parallelism"},{"slug":"nvidia-gpu","name":"nvidia gpu"},{"slug":"python","name":"python"},{"slug":"ubuntu-22-04","name":"ubuntu 22.04"}],"trust":{"provenance":{"is_fork":false,"github_id":876210014,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-07-11T23:10:11.273Z","maintenance":{"label":"Active","score":82,"methodology":"github_public_v1","releases_90d":0,"days_since_push":25,"last_release_at":null},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T23:10:11.662Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-07-12T03:39:55.782Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-07-12T03:39:55.782Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-07-12T03:39:55.782Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When you have an NVIDIA GPU with at least 8 GB of VRAM for inference or at least 22.5 GB to fine-tune models using LoRA (Low-Rank Adaptation) on a single GPU.","If you are working within Ubuntu 22.04 and require the ability to perform model parallelism by configuring `fsdp_devices`."],"when_not_to_use":["When your project is not compatible with Ubuntu 22.04 or if you do not have access to a supported GPU configuration.","If you need to support multi-node training, as this capability has yet to be implemented in the current version of openpi."],"source":"enrich:decision_facts","observed_at":"2026-07-12T03:38:17.441Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"openpi is a specialized tool for model training, inference & serving that leverages advanced GPU capabilities and has specific requirements for memory and hardware configurations."}]}}