--- title: "distributed-llama vs ggrun" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/b4rtaz-distributed-llama-vs-raketenkater-ggrun" tools: ["b4rtaz-distributed-llama", "raketenkater-ggrun"] --- # distributed-llama vs ggrun *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 ggrun if ggrun, an auto-tuned launcher for GGUF models using llama.cpp, offers OpenAI-compatible server support with multi-GPU tensor-split and MoE expert placement capabilities. [distributed-llama](https://github.com/b4rtaz/distributed-llama) reports 3.1k GitHub stars, 250 forks, and 48 open issues, last pushed Jul 5, 2026. [ggrun](https://github.com/raketenkater/ggrun) has 275 stars, 18 forks, and 4 open issues, last pushed Sep 19, 2026. Figures are from public GitHub metadata via [distributed-llama's repository](https://github.com/b4rtaz/distributed-llama) and [ggrun's repository](https://github.com/raketenkater/ggrun). | | [distributed-llama](/tools/b4rtaz-distributed-llama.md) | [ggrun](/tools/raketenkater-ggrun.md) | | --- | --- | --- | | Tagline | Distributed LLM inference using home devices cluster | Auto-tuned launcher for GGUF models on llama.cpp with OpenAI-compatible server | | Stars | 3,060 | 275 | | Forks | 250 | 18 | | Open issues | 48 | 4 | | Language | C++ | Go | | Adopt for | distributed-llama is a C++ framework that leverages multiple home devices for faster large language model inference, under the MIT license. | ggrun, an auto-tuned launcher for GGUF models using llama.cpp, offers OpenAI-compatible server support with multi-GPU tensor-split and MoE expert placement capabilities. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT License allows using ggrun freely in both open source and commercial projects, with conditions that the copyright notice and permission notice are preserved. | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [distributed-llama](/tools/b4rtaz-distributed-llama.md) | [ggrun](/tools/raketenkater-ggrun.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 76d | 0d | | Open issues (now) | 48 | 4 | | Stars delta | +48 (30d) | +11 (30d) | | Open issues delta | 0 (30d) | +3 (30d) | | Full report | [trust report](/tools/b4rtaz-distributed-llama/trust.md) | [trust report](/tools/raketenkater-ggrun/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: ggrun - **Pricing:** freemium - Free to use under MIT license; no direct costs involved in usage. - **Adopt for:** ggrun, an auto-tuned launcher for GGUF models using llama.cpp, offers OpenAI-compatible server support with multi-GPU tensor-split and MoE expert placement capabilities. - **License detail:** MIT License allows using ggrun freely in both open source and commercial projects, with conditions that the copyright notice and permission notice are preserved. ## Choose when ### Choose distributed-llama if… - distributed-llama is primarily C++; ggrun is Go. - 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 ggrun if… - ggrun is primarily Go; distributed-llama is C++. - Pricing: Free to use under MIT license; no direct costs involved in usage.. - Tags unique to ggrun: cuda, gguf, golang, inference-server. - When developing systems that require automatic hardware optimization and tuning for GGUF models on multiple GPUs ## 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 ggrun - For environments where single-GPU setups are preferred, as ggrun specializes in multi-GPU configurations and may offer limited advantage or additional complexity - When you do not require auto-tuning capabilities for hardware performance optimization since this feature is specific to ggrun ## Common questions ### What is the difference between distributed-llama and ggrun? distributed-llama: Distributed LLM inference using home devices cluster. ggrun: Auto-tuned launcher for GGUF models on llama.cpp with OpenAI-compatible server. See the comparison table for live GitHub stats and shared categories. ### When should I choose distributed-llama over ggrun? Choose distributed-llama over ggrun when distributed-llama is primarily C++; ggrun is Go; 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 ggrun over distributed-llama? Choose ggrun over distributed-llama when ggrun is primarily Go; distributed-llama is C++; Pricing: Free to use under MIT license; no direct costs involved in usage.; Tags unique to ggrun: cuda, gguf, golang, inference-server; When developing systems that require automatic hardware optimization and tuning for GGUF models on multiple GPUs. ### 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 ggrun? For environments where single-GPU setups are preferred, as ggrun specializes in multi-GPU configurations and may offer limited advantage or additional complexity When you do not require auto-tuning capabilities for hardware performance optimization since this feature is specific to ggrun ### Is distributed-llama or ggrun more popular on GitHub? distributed-llama has more GitHub stars (3,060 vs 275). Stars measure visibility, not whether either tool fits your constraints. ### Are distributed-llama and ggrun open source? Yes - both are open-source projects on GitHub (distributed-llama: MIT, ggrun: MIT). ### Where can I find alternatives to distributed-llama or ggrun? GraphCanon lists graph-backed alternatives at [distributed-llama alternatives](/tools/b4rtaz-distributed-llama/alternatives) and [ggrun alternatives](/tools/raketenkater-ggrun/alternatives) ([distributed-llama markdown twin](/tools/b4rtaz-distributed-llama/alternatives.md), [ggrun markdown twin](/tools/raketenkater-ggrun/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-raketenkater-ggrun.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, distributed-llama or ggrun? distributed-llama: Steady. ggrun: 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 ggrun? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [distributed-llama trust report](/tools/b4rtaz-distributed-llama/trust); [ggrun trust report](/tools/raketenkater-ggrun/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/_