--- title: "distributed-llama vs BodhiApp" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/b4rtaz-distributed-llama-vs-bodhisearch-bodhiapp" tools: ["b4rtaz-distributed-llama", "bodhisearch-bodhiapp"] --- # distributed-llama vs BodhiApp *GraphCanon updated Aug 24, 2026* ## Verdict Pick distributed-llama if distributed-llama is a C++ framework that leverages multiple home devices for faster large language model inference, under the MIT license; pick BodhiApp if bodhiApp streamlines local deployment of open-source and open-weight LLMs via Docker images, compatible with multiple hardware acceleration methods. [distributed-llama](https://github.com/b4rtaz/distributed-llama) reports 3.0k GitHub stars, 246 forks, and 48 open issues, last pushed Jul 5, 2026. [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 [distributed-llama's repository](https://github.com/b4rtaz/distributed-llama) and [BodhiApp's repository](https://github.com/BodhiSearch/BodhiApp). | | [distributed-llama](/tools/b4rtaz-distributed-llama.md) | [BodhiApp](/tools/bodhisearch-bodhiapp.md) | | --- | --- | --- | | Tagline | Distributed LLM inference using home devices cluster | Run Open Source/Open Weight LLMs locally with OpenAI compatible APIs | | Stars | 3,044 | 136 | | Forks | 246 | 10 | | Open issues | 48 | 10 | | Language | C++ | TypeScript | | Adopt for | distributed-llama is a C++ framework that leverages multiple home devices for faster large language model inference, under the MIT license. | 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 | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [distributed-llama](/tools/b4rtaz-distributed-llama.md) | [BodhiApp](/tools/bodhisearch-bodhiapp.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Active (82%) | | Days since push | 50d | 18d | | Open issues (now) | 48 | 10 | | Stars delta | +32 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Owner type | User | Organization | | Full report | [trust report](/tools/b4rtaz-distributed-llama/trust.md) | [trust report](/tools/bodhisearch-bodhiapp/trust.md) | ## Decision facts: distributed-llama - **Adopt for:** distributed-llama is a C++ framework that leverages multiple home devices for faster large language model inference, under the MIT license. ## 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 distributed-llama if… - distributed-llama is primarily C++; BodhiApp is TypeScript. - Tags unique to distributed-llama: distributed-computing, llm-inference, neural-network. - When you have multiple interconnected home devices and want to maximize their combined computing power for LLM inference tasks. ### Choose BodhiApp if… - BodhiApp is primarily TypeScript; distributed-llama is C++. - 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. - Also covers LLM Frameworks. - You need to deploy LLMs locally with flexible hardware support including AMD, NVIDIA GPUs, and CPUs. ## When NOT to use distributed-llama - For scenarios with fewer than two available devices, as the framework's capability to distribute and boost performance would be limited. - In professional environments that require strict data privacy controls, due to potential network vulnerabilities among home devices. ## 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 distributed-llama and BodhiApp? distributed-llama: Distributed LLM inference using home devices cluster. 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 distributed-llama over BodhiApp? Choose distributed-llama over BodhiApp when distributed-llama is primarily C++; BodhiApp is TypeScript; Tags unique to distributed-llama: distributed-computing, llm-inference, neural-network; When you have multiple interconnected home devices and want to maximize their combined computing power for LLM inference tasks. ### When should I choose BodhiApp over distributed-llama? Choose BodhiApp over distributed-llama when BodhiApp is primarily TypeScript; distributed-llama is C++; 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; Also covers LLM Frameworks; You need to deploy LLMs locally with flexible hardware support including AMD, NVIDIA GPUs, and CPUs. ### When should I avoid distributed-llama? For scenarios with fewer than two available devices, as the framework's capability to distribute and boost performance would be limited. In professional environments that require strict data privacy controls, due to potential network vulnerabilities among home devices. ### 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 distributed-llama or BodhiApp more popular on GitHub? distributed-llama has more GitHub stars (3,044 vs 136). Stars measure visibility, not whether either tool fits your constraints. ### Are distributed-llama and BodhiApp open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to distributed-llama or BodhiApp? GraphCanon lists graph-backed alternatives at [distributed-llama alternatives](/tools/b4rtaz-distributed-llama/alternatives) and [BodhiApp alternatives](/tools/bodhisearch-bodhiapp/alternatives) ([distributed-llama markdown twin](/tools/b4rtaz-distributed-llama/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/b4rtaz-distributed-llama-vs-bodhisearch-bodhiapp.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, distributed-llama or BodhiApp? distributed-llama: Steady. 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 distributed-llama and BodhiApp? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [distributed-llama trust report](/tools/b4rtaz-distributed-llama/trust); [BodhiApp trust report](/tools/bodhisearch-bodhiapp/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=b4rtaz-distributed-llama`](/api/graphcanon/graph?tool=b4rtaz-distributed-llama) - 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/_