--- title: "distributed-llama vs inference" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/b4rtaz-distributed-llama-vs-xorbitsai-inference" tools: ["b4rtaz-distributed-llama", "xorbitsai-inference"] --- # distributed-llama vs inference *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 inference if xinference is a unified production-ready inference API for deploying and running various models, including LLMs, on different environments. [distributed-llama](https://github.com/b4rtaz/distributed-llama) reports 3.1k GitHub stars, 250 forks, and 48 open issues, last pushed Jul 5, 2026. [inference](https://inference.readthedocs.io) has 9.6k stars, 866 forks, and 39 open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [distributed-llama's repository](https://github.com/b4rtaz/distributed-llama) and [inference's repository](https://github.com/xorbitsai/inference). | | [distributed-llama](/tools/b4rtaz-distributed-llama.md) | [inference](/tools/xorbitsai-inference.md) | | --- | --- | --- | | Tagline | Distributed LLM inference using home devices cluster | Unified production-ready inference API for various LLMs and models | | Stars | 3,060 | 9,575 | | Forks | 250 | 866 | | Open issues | 48 | 39 | | 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. | Xinference is a unified production-ready inference API for deploying and running various models, including LLMs, on different environments. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [distributed-llama](/tools/b4rtaz-distributed-llama.md) | [inference](/tools/xorbitsai-inference.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 76d | 0d | | Open issues (now) | 48 | 39 | | Stars delta | +48 (30d) | +105 (30d) | | Open issues delta | 0 (30d) | -3 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/b4rtaz-distributed-llama/trust.md) | [trust report](/tools/xorbitsai-inference/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: inference - **Pricing:** freemium - The tool is open-source under the Apache-2.0 license, but additional services or support might incur costs. - **Requirements:** Min 4 GB RAM; Requires Docker; Docker and CUDA setup are required for Nvidia GPU users.; For Kubernetes deployment, Helm is required. - **Adopt for:** Xinference is a unified production-ready inference API for deploying and running various models, including LLMs, on different environments. ## Choose when ### Choose distributed-llama if… - distributed-llama is primarily C++; inference is Python. - License: distributed-llama is MIT, inference 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 inference if… - inference is primarily Python; distributed-llama is C++. - License: inference is Apache-2.0, distributed-llama is MIT. - Pricing: The tool is open-source under the Apache-2.0 license, but additional services or support might incur costs.. - Requirements: Min 4 GB RAM; Requires Docker; Docker and CUDA setup are required for Nvidia GPU users.; For Kubernetes deployment, Helm is required.. - Tags unique to inference: artificial-intelligence, deployment, diffusers, gemma. - When you need a single API to deploy and run a variety of models, including LLMs, on cloud, on-premises, or local machines. ## 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 inference - If you are looking for a tool that supports only a specific type of model or environment, as Xinference is designed to be versatile across multiple models and deployment environments. - When you need a tool that does not require setting up Docker or Kubernetes for deployment, as Xinference provides detailed instructions for these setups but may not be suitable for users preferring a ## Common questions ### What is the difference between distributed-llama and inference? distributed-llama: Distributed LLM inference using home devices cluster. inference: Unified production-ready inference API for various LLMs and models. See the comparison table for live GitHub stats and shared categories. ### When should I choose distributed-llama over inference? Choose distributed-llama over inference when distributed-llama is primarily C++; inference is Python; License: distributed-llama is MIT, inference 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 inference over distributed-llama? Choose inference over distributed-llama when inference is primarily Python; distributed-llama is C++; License: inference is Apache-2.0, distributed-llama is MIT; Pricing: The tool is open-source under the Apache-2.0 license, but additional services or support might incur costs.; Requirements: Min 4 GB RAM; Requires Docker; Docker and CUDA setup are required for Nvidia GPU users.; For Kubernetes deployment, Helm is required.; Tags unique to inference: artificial-intelligence, deployment, diffusers, gemma; When you need a single API to deploy and run a variety of models, including LLMs, on cloud, on-premises, or local machines. ### 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 inference? If you are looking for a tool that supports only a specific type of model or environment, as Xinference is designed to be versatile across multiple models and deployment environments. When you need a tool that does not require setting up Docker or Kubernetes for deployment, as Xinference provides detailed instructions for these setups but may not be suitable for users preferring a ### Is distributed-llama or inference more popular on GitHub? inference has more GitHub stars (9,575 vs 3,060). Stars measure visibility, not whether either tool fits your constraints. ### Are distributed-llama and inference open source? Yes - both are open-source projects on GitHub (distributed-llama: MIT, inference: Apache-2.0). ### Where can I find alternatives to distributed-llama or inference? GraphCanon lists graph-backed alternatives at [distributed-llama alternatives](/tools/b4rtaz-distributed-llama/alternatives) and [inference alternatives](/tools/xorbitsai-inference/alternatives) ([distributed-llama markdown twin](/tools/b4rtaz-distributed-llama/alternatives.md), [inference markdown twin](/tools/xorbitsai-inference/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-xorbitsai-inference.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, distributed-llama or inference? distributed-llama: Steady. inference: 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 inference? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [distributed-llama trust report](/tools/b4rtaz-distributed-llama/trust); [inference trust report](/tools/xorbitsai-inference/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/_