--- title: "distributed-llama vs omlx" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/b4rtaz-distributed-llama-vs-jundot-omlx" tools: ["b4rtaz-distributed-llama", "jundot-omlx"] --- # distributed-llama vs omlx *GraphCanon updated Sep 20, 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 omlx if omlx is an LLM inference server tailored for Apple Silicon that emphasizes continuous batching and SSD caching capabilities, accessible through macOS menu bar control or Homebrew installation. [distributed-llama](https://github.com/b4rtaz/distributed-llama) reports 3.1k GitHub stars, 250 forks, and 48 open issues, last pushed Jul 5, 2026. [omlx](https://omlx.ai) has 22k stars, 1.9k forks, and 1.4k open issues, last pushed Sep 20, 2026. Figures are from public GitHub metadata via [distributed-llama's repository](https://github.com/b4rtaz/distributed-llama) and [omlx's repository](https://github.com/jundot/omlx). | | [distributed-llama](/tools/b4rtaz-distributed-llama.md) | [omlx](/tools/jundot-omlx.md) | | --- | --- | --- | | Tagline | Distributed LLM inference using home devices cluster | LLM inference server with continuous batching and SSD caching for Apple Silicon | | Stars | 3,060 | 21,934 | | Forks | 250 | 1,899 | | Open issues | 48 | 1,407 | | Language | C++ | Python | | Adopt for | distributed-llama is a C++ framework that leverages multiple home devices for faster large language model inference, under the MIT license. | omlx is an LLM inference server tailored for Apple Silicon that emphasizes continuous batching and SSD caching capabilities, accessible through macOS menu bar control or Homebrew installation. | | Persona | - | - | | Runtime | - | - | | License | MIT | omlx is available under the Apache License, Version 2.0 (Apache-2.0), a permissive free software license. | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [distributed-llama](/tools/b4rtaz-distributed-llama.md) | [omlx](/tools/jundot-omlx.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 76d | 0d | | Open issues (now) | 48 | 1.4k | | Stars delta | +48 (30d) | +3.3k (30d) | | Open issues delta | 0 (30d) | +552 (30d) | | Full report | [trust report](/tools/b4rtaz-distributed-llama/trust.md) | [trust report](/tools/jundot-omlx/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: omlx - **Adopt for:** omlx is an LLM inference server tailored for Apple Silicon that emphasizes continuous batching and SSD caching capabilities, accessible through macOS menu bar control or Homebrew installation. - **License detail:** omlx is available under the Apache License, Version 2.0 (Apache-2.0), a permissive free software license. ## Choose when ### Choose distributed-llama if… - distributed-llama is primarily C++; omlx is Python. - License: distributed-llama is MIT, omlx is Apache-2.0. - 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 omlx if… - omlx is primarily Python; distributed-llama is C++. - License: omlx is Apache-2.0, distributed-llama is MIT. - Tags unique to omlx: apple-silicon, inference-server, llm, macos. - If your primary computing environment is based on Apple Silicon devices, omlx offers optimized performance for running large language model inferences. ## 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 omlx - If your development infrastructure relies on non-Apple Silicon hardware, omlx's specific optimizations will not be as beneficial. - Teams that require cross-platform compatibility or run servers predominantly on non-macOS operating systems should consider alternatives with broader support. - For environments where direct control through the menu bar is not practical or desired. ## Common questions ### What is the difference between distributed-llama and omlx? distributed-llama: Distributed LLM inference using home devices cluster. omlx: LLM inference server with continuous batching and SSD caching for Apple Silicon. See the comparison table for live GitHub stats and shared categories. ### When should I choose distributed-llama over omlx? Choose distributed-llama over omlx when distributed-llama is primarily C++; omlx is Python; License: distributed-llama is MIT, omlx is Apache-2.0; 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 omlx over distributed-llama? Choose omlx over distributed-llama when omlx is primarily Python; distributed-llama is C++; License: omlx is Apache-2.0, distributed-llama is MIT; Tags unique to omlx: apple-silicon, inference-server, llm, macos; If your primary computing environment is based on Apple Silicon devices, omlx offers optimized performance for running large language model inferences. ### 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 omlx? If your development infrastructure relies on non-Apple Silicon hardware, omlx's specific optimizations will not be as beneficial. Teams that require cross-platform compatibility or run servers predominantly on non-macOS operating systems should consider alternatives with broader support. For environments where direct control through the menu bar is not practical or desired. ### Is distributed-llama or omlx more popular on GitHub? omlx has more GitHub stars (21,934 vs 3,060). Stars measure visibility, not whether either tool fits your constraints. ### Are distributed-llama and omlx open source? Yes - both are open-source projects on GitHub (distributed-llama: MIT, omlx: Apache-2.0). ### Where can I find alternatives to distributed-llama or omlx? GraphCanon lists graph-backed alternatives at [distributed-llama alternatives](/tools/b4rtaz-distributed-llama/alternatives) and [omlx alternatives](/tools/jundot-omlx/alternatives) ([distributed-llama markdown twin](/tools/b4rtaz-distributed-llama/alternatives.md), [omlx markdown twin](/tools/jundot-omlx/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-jundot-omlx.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, distributed-llama or omlx? distributed-llama: Steady. omlx: 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 omlx? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [distributed-llama trust report](/tools/b4rtaz-distributed-llama/trust); [omlx trust report](/tools/jundot-omlx/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/_