--- title: "distributed-llama vs Foundry-Local" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/b4rtaz-distributed-llama-vs-microsoft-foundry-local" tools: ["b4rtaz-distributed-llama", "microsoft-foundry-local"] --- # distributed-llama vs Foundry-Local *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 Foundry-Local if foundry-Local offers SDK and CLI for local GPU-accelerated AI inference, focusing on speech-to-text models such as Whisper, using ONNX Runtime. [distributed-llama](https://github.com/b4rtaz/distributed-llama) reports 3.0k GitHub stars, 246 forks, and 48 open issues, last pushed Jul 5, 2026. [Foundry-Local](https://foundrylocal.ai) has 2.5k stars, 348 forks, and 71 open issues, last pushed Jul 30, 2026. Figures are from public GitHub metadata via [distributed-llama's repository](https://github.com/b4rtaz/distributed-llama) and [Foundry-Local's repository](https://github.com/microsoft/Foundry-Local). | | [distributed-llama](/tools/b4rtaz-distributed-llama.md) | [Foundry-Local](/tools/microsoft-foundry-local.md) | | --- | --- | --- | | Tagline | Distributed LLM inference using home devices cluster | SDK and CLI for local AI inference with GPU acceleration, supporting speech-to-text models like Whisper. | | Stars | 3,044 | 2,479 | | Forks | 246 | 348 | | Open issues | 48 | 71 | | Language | C++ | C++ | | Adopt for | distributed-llama is a C++ framework that leverages multiple home devices for faster large language model inference, under the MIT license. | Foundry-Local offers SDK and CLI for local GPU-accelerated AI inference, focusing on speech-to-text models such as Whisper, using ONNX Runtime. | | Persona | - | - | | Runtime | - | - | | License | MIT | SDK is licensed under the MIT License. The CLI uses the Microsoft Software License Terms. Models available with Foundry-Local are individually licensed as per their documentation or download page. | | Categories | Inference & Serving | Inference & Serving, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [distributed-llama](/tools/b4rtaz-distributed-llama.md) | [Foundry-Local](/tools/microsoft-foundry-local.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 50d | 0d | | Open issues (now) | 48 | 71 | | 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/microsoft-foundry-local/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: Foundry-Local - **Pricing:** freemium - Free to use SDK and CLI under MIT and Microsoft licenses, individual AI models subject to their own license terms. - **Adopt for:** Foundry-Local offers SDK and CLI for local GPU-accelerated AI inference, focusing on speech-to-text models such as Whisper, using ONNX Runtime. - **License detail:** SDK is licensed under the MIT License. The CLI uses the Microsoft Software License Terms. Models available with Foundry-Local are individually licensed as per their documentation or download page. ## Choose when ### Choose distributed-llama if… - License: distributed-llama is MIT, Foundry-Local is Other. - 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 Foundry-Local if… - License: Foundry-Local is Other, distributed-llama is MIT. - Pricing: Free to use SDK and CLI under MIT and Microsoft licenses, individual AI models subject to their own license terms.. - Tags unique to Foundry-Local: ai-sdk, chat-completions, foundry-local, gpu acceleration. - Also covers Speech & Audio. - Need to run AI models locally with GPU acceleration ## 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 Foundry-Local - Require cloud-based inference services for resource-intensive tasks - Prefer using models not supported by ONNX Runtime or Whisper ## Common questions ### What is the difference between distributed-llama and Foundry-Local? distributed-llama: Distributed LLM inference using home devices cluster. Foundry-Local: SDK and CLI for local AI inference with GPU acceleration, supporting speech-to-text models like Whisper.. See the comparison table for live GitHub stats and shared categories. ### When should I choose distributed-llama over Foundry-Local? Choose distributed-llama over Foundry-Local when License: distributed-llama is MIT, Foundry-Local is Other; 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 Foundry-Local over distributed-llama? Choose Foundry-Local over distributed-llama when License: Foundry-Local is Other, distributed-llama is MIT; Pricing: Free to use SDK and CLI under MIT and Microsoft licenses, individual AI models subject to their own license terms.; Tags unique to Foundry-Local: ai-sdk, chat-completions, foundry-local, gpu acceleration; Also covers Speech & Audio; Need to run AI models locally with GPU acceleration. ### 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 Foundry-Local? Require cloud-based inference services for resource-intensive tasks Prefer using models not supported by ONNX Runtime or Whisper ### Is distributed-llama or Foundry-Local more popular on GitHub? distributed-llama has more GitHub stars (3,044 vs 2,479). Stars measure visibility, not whether either tool fits your constraints. ### Are distributed-llama and Foundry-Local open source? Yes - both are open-source projects on GitHub (distributed-llama: MIT, Foundry-Local: Other). ### Where can I find alternatives to distributed-llama or Foundry-Local? GraphCanon lists graph-backed alternatives at [distributed-llama alternatives](/tools/b4rtaz-distributed-llama/alternatives) and [Foundry-Local alternatives](/tools/microsoft-foundry-local/alternatives) ([distributed-llama markdown twin](/tools/b4rtaz-distributed-llama/alternatives.md), [Foundry-Local markdown twin](/tools/microsoft-foundry-local/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-microsoft-foundry-local.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, distributed-llama or Foundry-Local? distributed-llama: Steady. Foundry-Local: Very 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 Foundry-Local? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [distributed-llama trust report](/tools/b4rtaz-distributed-llama/trust); [Foundry-Local trust report](/tools/microsoft-foundry-local/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/_