--- title: "distributed-llama vs xllm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/b4rtaz-distributed-llama-vs-xllm-ai-xllm" tools: ["b4rtaz-distributed-llama", "xllm-ai-xllm"] --- # distributed-llama vs xllm *GraphCanon updated Aug 25, 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 xllm if a high-performance inference engine for LLM, VLM, DiT, and REC models by the OpenAtom Foundation. [distributed-llama](https://github.com/b4rtaz/distributed-llama) reports 3.0k GitHub stars, 246 forks, and 48 open issues, last pushed Jul 5, 2026. [xllm](https://xllm-ai.com/) has 1.5k stars, 282 forks, and 213 open issues, last pushed Aug 24, 2026. Figures are from public GitHub metadata via [distributed-llama's repository](https://github.com/b4rtaz/distributed-llama) and [xllm's repository](https://github.com/xLLM-AI/xllm). | | [distributed-llama](/tools/b4rtaz-distributed-llama.md) | [xllm](/tools/xllm-ai-xllm.md) | | --- | --- | --- | | Tagline | Distributed LLM inference using home devices cluster | A high-performance inference engine for LLM, VLM, DiT and REC models | | Stars | 3,044 | 1,534 | | Forks | 246 | 282 | | Open issues | 48 | 213 | | 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. | A high-performance inference engine for LLM, VLM, DiT, and REC models by the OpenAtom Foundation. | | 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) | [xllm](/tools/xllm-ai-xllm.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 50d | 0d | | Open issues (now) | 48 | 213 | | Stars delta | +32 (30d) | +41 (30d) | | Open issues delta | 0 (30d) | +22 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/b4rtaz-distributed-llama/trust.md) | [trust report](/tools/xllm-ai-xllm/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: xllm - **Adopt for:** A high-performance inference engine for LLM, VLM, DiT, and REC models by the OpenAtom Foundation. ## Choose when ### Choose distributed-llama if… - License: distributed-llama is MIT, xllm is Apache-2.0. - Tags unique to distributed-llama: distributed-computing, neural-network. - When you have multiple interconnected home devices and want to maximize their combined computing power for LLM inference tasks. ### Choose xllm if… - License: xllm is Apache-2.0, distributed-llama is MIT. - Tags unique to xllm: deepseek, glm. - When developing applications that require optimized performance on various AI accelerators ## 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 xllm - If your project strictly requires Python-based inference engines for backend support - In cases preferring proprietary licenses over the Apache-2.0 open-source framework used here ## Common questions ### What is the difference between distributed-llama and xllm? distributed-llama: Distributed LLM inference using home devices cluster. xllm: A high-performance inference engine for LLM, VLM, DiT and REC models. See the comparison table for live GitHub stats and shared categories. ### When should I choose distributed-llama over xllm? Choose distributed-llama over xllm when License: distributed-llama is MIT, xllm is Apache-2.0; Tags unique to distributed-llama: distributed-computing, 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 xllm over distributed-llama? Choose xllm over distributed-llama when License: xllm is Apache-2.0, distributed-llama is MIT; Tags unique to xllm: deepseek, glm; When developing applications that require optimized performance on various AI accelerators. ### 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 xllm? If your project strictly requires Python-based inference engines for backend support In cases preferring proprietary licenses over the Apache-2.0 open-source framework used here ### Is distributed-llama or xllm more popular on GitHub? distributed-llama has more GitHub stars (3,044 vs 1,534). Stars measure visibility, not whether either tool fits your constraints. ### Are distributed-llama and xllm open source? Yes - both are open-source projects on GitHub (distributed-llama: MIT, xllm: Apache-2.0). ### Where can I find alternatives to distributed-llama or xllm? GraphCanon lists graph-backed alternatives at [distributed-llama alternatives](/tools/b4rtaz-distributed-llama/alternatives) and [xllm alternatives](/tools/xllm-ai-xllm/alternatives) ([distributed-llama markdown twin](/tools/b4rtaz-distributed-llama/alternatives.md), [xllm markdown twin](/tools/xllm-ai-xllm/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-xllm-ai-xllm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, distributed-llama or xllm? distributed-llama: Steady. xllm: 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 xllm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [distributed-llama trust report](/tools/b4rtaz-distributed-llama/trust); [xllm trust report](/tools/xllm-ai-xllm/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/_