--- title: "openpi vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/physical-intelligence-openpi-vs-tensorchord-awesome-llmops" tools: ["physical-intelligence-openpi", "tensorchord-awesome-llmops"] --- # openpi vs Awesome-LLMOps *GraphCanon updated Sep 20, 2026* ## Verdict 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; pick Awesome-LLMOps if awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. [openpi](https://github.com/Physical-Intelligence/openpi) reports 14k GitHub stars, 2.5k forks, and 345 open issues, last pushed Aug 24, 2026. [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) has 5.9k stars, 1.1k forks, and 317 open issues, last pushed May 21, 2026. Figures are from public GitHub metadata via [openpi's repository](https://github.com/Physical-Intelligence/openpi) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [openpi](/tools/physical-intelligence-openpi.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | Repository for running AI models with GPU requirements specified for inference and fine-tuning. | An awesome & curated list of best LLMOps tools for developers | | Stars | 13,874 | 5,941 | | Forks | 2,467 | 1,058 | | Open issues | 345 | 317 | | Language | Python | Shell | | 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. | Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | CC0-1.0 | | Categories | Inference & Serving, Model Training | Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [openpi](/tools/physical-intelligence-openpi.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Active (82%) | Slowing (36%) | | Days since push | 24d | 121d | | Open issues (now) | 345 | 317 | | Stars delta | +776 (30d) | +26 (30d) | | Open issues delta | +29 (30d) | +70 (30d) | | Full report | [trust report](/tools/physical-intelligence-openpi/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## 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. ## Decision facts: Awesome-LLMOps - **Adopt for:** Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. ## Choose when ### Choose openpi if… - openpi is primarily Python; Awesome-LLMOps is Shell. - License: openpi is Apache-2.0, Awesome-LLMOps is CC0-1.0. - 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: fine-tuning, gpu, inference, lora. - 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. ### Choose Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; openpi is Python. - License: Awesome-LLMOps is CC0-1.0, openpi is Apache-2.0. - Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, mlops. - Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, LLM Frameworks, Speech & Audio. - - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ## 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. ## When NOT to use Awesome-LLMOps - - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources. ## Common questions ### What is the difference between openpi and Awesome-LLMOps? openpi: Repository for running AI models with GPU requirements specified for inference and fine-tuning.. Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. See the comparison table for live GitHub stats and shared categories. ### When should I choose openpi over Awesome-LLMOps? Choose openpi over Awesome-LLMOps when openpi is primarily Python; Awesome-LLMOps is Shell; License: openpi is Apache-2.0, Awesome-LLMOps is CC0-1.0; 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: fine-tuning, gpu, inference, lora; 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 choose Awesome-LLMOps over openpi? Choose Awesome-LLMOps over openpi when Awesome-LLMOps is primarily Shell; openpi is Python; License: Awesome-LLMOps is CC0-1.0, openpi is Apache-2.0; Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, LLM Frameworks, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ### 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. ### When should I avoid Awesome-LLMOps? - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources. ### Is openpi or Awesome-LLMOps more popular on GitHub? openpi has more GitHub stars (13,874 vs 5,941). Stars measure visibility, not whether either tool fits your constraints. ### Are openpi and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub (openpi: Apache-2.0, Awesome-LLMOps: CC0-1.0). ### Where can I find alternatives to openpi or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [openpi alternatives](/tools/physical-intelligence-openpi/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([openpi markdown twin](/tools/physical-intelligence-openpi/alternatives.md), [Awesome-LLMOps markdown twin](/tools/tensorchord-awesome-llmops/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/physical-intelligence-openpi-vs-tensorchord-awesome-llmops.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, openpi or Awesome-LLMOps? openpi: Active. Awesome-LLMOps: Slowing. 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 openpi and Awesome-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [openpi trust report](/tools/physical-intelligence-openpi/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=physical-intelligence-openpi`](/api/graphcanon/graph?tool=physical-intelligence-openpi) - 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/_