--- title: "pmetal vs openpi" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/epistates-pmetal-vs-physical-intelligence-openpi" tools: ["epistates-pmetal", "physical-intelligence-openpi"] --- # pmetal vs openpi *GraphCanon updated Sep 20, 2026* ## Verdict Pick pmetal if specializes in high-performance local Large Language Model inference and fine-tuning on Apple Silicon hardware using MLX/Metal; pick openpi if openpi is a repository for running AI models with specific GPU requirements for inference and fine-tuning, including LoRA and full fine-tuning modes. [pmetal](https://pmetal.io) reports 317 GitHub stars, 26 forks, and 8 open issues, last pushed Sep 17, 2026. [openpi](https://github.com/Physical-Intelligence/openpi) has 14k stars, 2.5k forks, and 345 open issues, last pushed Aug 24, 2026. Figures are from public GitHub metadata via [pmetal's repository](https://github.com/Epistates/pmetal) and [openpi's repository](https://github.com/Physical-Intelligence/openpi). | | [pmetal](/tools/epistates-pmetal.md) | [openpi](/tools/physical-intelligence-openpi.md) | | --- | --- | --- | | Tagline | High-performance Apple Silicon framework for LLM inference and fine-tuning | Repository for running AI models with GPU requirements specified for inference and fine-tuning. | | Stars | 317 | 13,874 | | Forks | 26 | 2,467 | | Open issues | 8 | 345 | | Language | Rust | Python | | Adopt for | Specializes in high-performance local Large Language Model inference and fine-tuning on Apple Silicon hardware using MLX/Metal. | openpi is a repository for running AI models with specific GPU requirements for inference and fine-tuning, including LoRA and full fine-tuning modes. | | Persona | - | - | | Runtime | - | - | | License | Dual-licensed under MIT or Apache-2.0, offering flexible open-source options for commercial and non-commercial projects alike. | Apache-2.0 | | Categories | Inference & Serving, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [pmetal](/tools/epistates-pmetal.md) | [openpi](/tools/physical-intelligence-openpi.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 2d | 24d | | Open issues (now) | 8 | 345 | | Stars delta | +11 (30d) | +776 (30d) | | Open issues delta | -1 (30d) | +29 (30d) | | Full report | [trust report](/tools/epistates-pmetal/trust.md) | [trust report](/tools/physical-intelligence-openpi/trust.md) | ## Decision facts: pmetal - **Adopt for:** Specializes in high-performance local Large Language Model inference and fine-tuning on Apple Silicon hardware using MLX/Metal. - **License detail:** Dual-licensed under MIT or Apache-2.0, offering flexible open-source options for commercial and non-commercial projects alike. ## Decision facts: openpi - **Requirements:** The repository requires an NVIDIA GPU with at least 8 GB of memory for inference, 22.5 GB for fine-tuning with LoRA, and 70 GB for full fine-tuning. - **Adopt for:** openpi is a repository for running AI models with specific GPU requirements for inference and fine-tuning, including LoRA and full fine-tuning modes. ## Choose when ### Choose pmetal if… - pmetal is primarily Rust; openpi is Python. - License: pmetal is Other, openpi is Apache-2.0. - Tags unique to pmetal: ai, ane, apple-silicon, deep-learning. - For optimal performance on Apple M1-M5 series, when leveraging GPU and ANE for LLMs is crucial. ### Choose openpi if… - openpi is primarily Python; pmetal is Rust. - License: openpi is Apache-2.0, pmetal is Other. - Requirements: The repository requires an NVIDIA GPU with at least 8 GB of memory for inference, 22.5 GB for fine-tuning with LoRA, and 70 GB for full fine-tuning.. - Tags unique to openpi: gpu, inference, lora, model parallelism. - Use openpi when you need to run AI models with precise GPU memory requirements for inference and fine-tuning, particularly with LoRA and full fine-tuning modes. ## When NOT to use pmetal - Avoid if support for Nvidia GPUs or Intel CPUs is needed. - Not suitable when flexibility in language models exceeds pmetal's capabilities with only specific transformer models supported natively. - Steer clear if the project environment does not support Rust or if Apple-specific hardware acceleration is unnecessary. ## When NOT to use openpi - Avoid using openpi if your project requires multi-node training, as the current training script does not support this feature. - Do not use openpi if you are working on an operating system other than Ubuntu 22.04, as the repository has not been tested with other systems. ## Common questions ### What is the difference between pmetal and openpi? pmetal: High-performance Apple Silicon framework for LLM inference and fine-tuning. openpi: Repository for running AI models with GPU requirements specified for inference and fine-tuning.. See the comparison table for live GitHub stats and shared categories. ### When should I choose pmetal over openpi? Choose pmetal over openpi when pmetal is primarily Rust; openpi is Python; License: pmetal is Other, openpi is Apache-2.0; Tags unique to pmetal: ai, ane, apple-silicon, deep-learning; For optimal performance on Apple M1-M5 series, when leveraging GPU and ANE for LLMs is crucial. ### When should I choose openpi over pmetal? Choose openpi over pmetal when openpi is primarily Python; pmetal is Rust; License: openpi is Apache-2.0, pmetal is Other; Requirements: The repository requires an NVIDIA GPU with at least 8 GB of memory for inference, 22.5 GB for fine-tuning with LoRA, and 70 GB for full fine-tuning.; Tags unique to openpi: gpu, inference, lora, model parallelism; Use openpi when you need to run AI models with precise GPU memory requirements for inference and fine-tuning, particularly with LoRA and full fine-tuning modes. ### When should I avoid pmetal? Avoid if support for Nvidia GPUs or Intel CPUs is needed. Not suitable when flexibility in language models exceeds pmetal's capabilities with only specific transformer models supported natively. Steer clear if the project environment does not support Rust or if Apple-specific hardware acceleration is unnecessary. ### When should I avoid openpi? Avoid using openpi if your project requires multi-node training, as the current training script does not support this feature. Do not use openpi if you are working on an operating system other than Ubuntu 22.04, as the repository has not been tested with other systems. ### Is pmetal or openpi more popular on GitHub? openpi has more GitHub stars (13,874 vs 317). Stars measure visibility, not whether either tool fits your constraints. ### Are pmetal and openpi open source? Yes - both are open-source projects on GitHub (pmetal: Other, openpi: Apache-2.0). ### Where can I find alternatives to pmetal or openpi? GraphCanon lists graph-backed alternatives at [pmetal alternatives](/tools/epistates-pmetal/alternatives) and [openpi alternatives](/tools/physical-intelligence-openpi/alternatives) ([pmetal markdown twin](/tools/epistates-pmetal/alternatives.md), [openpi markdown twin](/tools/physical-intelligence-openpi/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/epistates-pmetal-vs-physical-intelligence-openpi.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, pmetal or openpi? pmetal: Very active. openpi: Active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for pmetal and openpi? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [pmetal trust report](/tools/epistates-pmetal/trust); [openpi trust report](/tools/physical-intelligence-openpi/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=epistates-pmetal`](/api/graphcanon/graph?tool=epistates-pmetal) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_