--- title: "petals vs BodhiApp" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bigscience-workshop-petals-vs-bodhisearch-bodhiapp" tools: ["bigscience-workshop-petals", "bodhisearch-bodhiapp"] --- # petals vs BodhiApp *GraphCanon updated Aug 17, 2026* ## Verdict Pick petals if petals is designed for users aiming to run large language models at home with potential speedups through a distributed, BitTorrent-style peer-to-peer network; pick BodhiApp if bodhiApp streamlines local deployment of open-source and open-weight LLMs via Docker images, compatible with multiple hardware acceleration methods. [petals](https://petals.dev) reports 10k GitHub stars, 642 forks, and 113 open issues, last pushed Sep 7, 2024. [BodhiApp](https://getbodhi.app/) has 136 stars, 10 forks, and 10 open issues, last pushed Jul 26, 2026. Figures are from public GitHub metadata via [petals's repository](https://github.com/bigscience-workshop/petals) and [BodhiApp's repository](https://github.com/BodhiSearch/BodhiApp). | | [petals](/tools/bigscience-workshop-petals.md) | [BodhiApp](/tools/bodhisearch-bodhiapp.md) | | --- | --- | --- | | Tagline | Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading | Run Open Source/Open Weight LLMs locally with OpenAI compatible APIs | | Stars | 10,496 | 136 | | Forks | 642 | 10 | | Open issues | 113 | 10 | | Language | Python | TypeScript | | Adopt for | Petals is designed for users aiming to run large language models at home with potential speedups through a distributed, BitTorrent-style peer-to-peer network. | BodhiApp streamlines local deployment of open-source and open-weight LLMs via Docker images, compatible with multiple hardware acceleration methods. | | Persona | - | - | | Runtime | - | - | | License | MIT | The license information for BodhiApp has not been provided. | | Categories | Inference & Serving, LLM Frameworks | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [petals](/tools/bigscience-workshop-petals.md) | [BodhiApp](/tools/bodhisearch-bodhiapp.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 708d | 18d | | Open issues (now) | 113 | 10 | | Stars delta | +212 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/bigscience-workshop-petals/trust.md) | [trust report](/tools/bodhisearch-bodhiapp/trust.md) | ## Decision facts: petals - **Adopt for:** Petals is designed for users aiming to run large language models at home with potential speedups through a distributed, BitTorrent-style peer-to-peer network. ## Decision facts: BodhiApp - **Pricing:** unknown - Pricing details are not mentioned in the repository data. - **Requirements:** Requires Docker; Requires Docker environment. Specific model requirements vary depending on the hardware variant chosen. - **Adopt for:** BodhiApp streamlines local deployment of open-source and open-weight LLMs via Docker images, compatible with multiple hardware acceleration methods. - **License detail:** The license information for BodhiApp has not been provided. ## Choose when ### Choose petals if… - petals is primarily Python; BodhiApp is TypeScript. - Tags unique to petals: bloom, chatbot, deep-learning, distributed-systems. - petals ships Docker support for self-hosted deployment. - - When you want to leverage faster fine-tuning and inference of LLMs (up to 10x) by utilizing distributed layers across a network similar to a BitTorrent system. ### Choose BodhiApp if… - BodhiApp is primarily TypeScript; petals is Python. - Pricing: Pricing details are not mentioned in the repository data.. - Requirements: Requires Docker; Requires Docker environment. Specific model requirements vary depending on the hardware variant chosen.. - Tags unique to BodhiApp: gemma, generative-ai, llama, llm. - You need to deploy LLMs locally with flexible hardware support including AMD, NVIDIA GPUs, and CPUs. ## When NOT to use petals - - When your use case strictly requires on-premises computation and you do not wish to rely on external peer-to-peer distributed networks, as Petals' efficiency comes with a dependency on its network. - - If you need absolute control over the data privacy and don't trust the decentralized system for sensitive information processing; petals uses a volunteer-computing model which might have variable or ## When NOT to use BodhiApp - Your project strictly requires non-local deployment options, as BodhiApp focuses on local hosting of models. - If your environment is limited to unsupported GPU hardware or lacks adequate drivers for CUDA, ROCm, or Vulkan acceleration methods. - You need support beyond Mac platforms as BodhiApp does not yet provide installation instructions for other operating systems. ## Common questions ### What is the difference between petals and BodhiApp? petals: Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading. BodhiApp: Run Open Source/Open Weight LLMs locally with OpenAI compatible APIs. See the comparison table for live GitHub stats and shared categories. ### When should I choose petals over BodhiApp? Choose petals over BodhiApp when petals is primarily Python; BodhiApp is TypeScript; Tags unique to petals: bloom, chatbot, deep-learning, distributed-systems; petals ships Docker support for self-hosted deployment; - When you want to leverage faster fine-tuning and inference of LLMs (up to 10x) by utilizing distributed layers across a network similar to a BitTorrent system. ### When should I choose BodhiApp over petals? Choose BodhiApp over petals when BodhiApp is primarily TypeScript; petals is Python; Pricing: Pricing details are not mentioned in the repository data.; Requirements: Requires Docker; Requires Docker environment. Specific model requirements vary depending on the hardware variant chosen.; Tags unique to BodhiApp: gemma, generative-ai, llama, llm; You need to deploy LLMs locally with flexible hardware support including AMD, NVIDIA GPUs, and CPUs. ### When should I avoid petals? - When your use case strictly requires on-premises computation and you do not wish to rely on external peer-to-peer distributed networks, as Petals' efficiency comes with a dependency on its network. - If you need absolute control over the data privacy and don't trust the decentralized system for sensitive information processing; petals uses a volunteer-computing model which might have variable or ### When should I avoid BodhiApp? Your project strictly requires non-local deployment options, as BodhiApp focuses on local hosting of models. If your environment is limited to unsupported GPU hardware or lacks adequate drivers for CUDA, ROCm, or Vulkan acceleration methods. You need support beyond Mac platforms as BodhiApp does not yet provide installation instructions for other operating systems. ### Is petals or BodhiApp more popular on GitHub? petals has more GitHub stars (10,496 vs 136). Stars measure visibility, not whether either tool fits your constraints. ### Are petals and BodhiApp open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to petals or BodhiApp? GraphCanon lists graph-backed alternatives at [petals alternatives](/tools/bigscience-workshop-petals/alternatives) and [BodhiApp alternatives](/tools/bodhisearch-bodhiapp/alternatives) ([petals markdown twin](/tools/bigscience-workshop-petals/alternatives.md), [BodhiApp markdown twin](/tools/bodhisearch-bodhiapp/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/bigscience-workshop-petals-vs-bodhisearch-bodhiapp.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, petals or BodhiApp? petals: Dormant. BodhiApp: 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 petals and BodhiApp? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [petals trust report](/tools/bigscience-workshop-petals/trust); [BodhiApp trust report](/tools/bodhisearch-bodhiapp/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bigscience-workshop-petals`](/api/graphcanon/graph?tool=bigscience-workshop-petals) - 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/_