--- title: "distributed-llama vs deploy-llms-with-ansible" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/b4rtaz-distributed-llama-vs-xamey-deploy-llms-with-ansible" tools: ["b4rtaz-distributed-llama", "xamey-deploy-llms-with-ansible"] --- # distributed-llama vs deploy-llms-with-ansible *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 deploy-llms-with-ansible if deploy-llms-with-ansible. [distributed-llama](https://github.com/b4rtaz/distributed-llama) reports 3.0k GitHub stars, 246 forks, and 48 open issues, last pushed Jul 5, 2026. [deploy-llms-with-ansible](https://github.com/xamey/deploy-llms-with-ansible) has 3 stars, 0 forks, and 0 open issues, last pushed May 1, 2025. Figures are from public GitHub metadata via [distributed-llama's repository](https://github.com/b4rtaz/distributed-llama) and [deploy-llms-with-ansible's repository](https://github.com/xamey/deploy-llms-with-ansible). | | [distributed-llama](/tools/b4rtaz-distributed-llama.md) | [deploy-llms-with-ansible](/tools/xamey-deploy-llms-with-ansible.md) | | --- | --- | --- | | Tagline | Distributed LLM inference using home devices cluster | Easily deploy LLMs using Ansible | | Stars | 3,044 | 3 | | Forks | 246 | 0 | | Open issues | 48 | 0 | | Language | C++ | - | | Adopt for | distributed-llama is a C++ framework that leverages multiple home devices for faster large language model inference, under the MIT license. | deploy-llms-with-ansible | | Persona | - | - | | Runtime | - | - | | License | MIT | - | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [distributed-llama](/tools/b4rtaz-distributed-llama.md) | [deploy-llms-with-ansible](/tools/xamey-deploy-llms-with-ansible.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 50d | 462d | | Open issues (now) | 48 | 0 | | Stars delta | +32 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/b4rtaz-distributed-llama/trust.md) | [trust report](/tools/xamey-deploy-llms-with-ansible/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: deploy-llms-with-ansible - **Pricing:** unknown - **Requirements:** Requires Docker; Requires Ansible installed and configured on the local machine.; Debian-based VM with SSH access and Docker must be present. - **Adopt for:** deploy-llms-with-ansible ## Choose when ### Choose distributed-llama if… - 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. - More GitHub stars (3.0k vs 3) - visibility, not fit. ### Choose deploy-llms-with-ansible if… - Requirements: Requires Docker; Requires Ansible installed and configured on the local machine.; Debian-based VM with SSH access and Docker must be present.. - Tags unique to deploy-llms-with-ansible: ansible, deployment, docker, llama-cpp. - When you prefer using Ansible to automate the deployment of LLMs on a Debian-based virtual machine equipped with Docker. ## 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 deploy-llms-with-ansible - When working in an environment that uses alternative automation tools like Terraform or Chef, as this tool specifically requires Ansible knowledge. - If the infrastructure does not support or permit the use of Docker for containerizing applications. - In cases where extensive customization of models beyond what llama.cpp and Ollama offer is required. ## Common questions ### What is the difference between distributed-llama and deploy-llms-with-ansible? distributed-llama: Distributed LLM inference using home devices cluster. deploy-llms-with-ansible: Easily deploy LLMs using Ansible. See the comparison table for live GitHub stats and shared categories. ### When should I choose distributed-llama over deploy-llms-with-ansible? Choose distributed-llama over deploy-llms-with-ansible when 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; More GitHub stars (3.0k vs 3) - visibility, not fit. ### When should I choose deploy-llms-with-ansible over distributed-llama? Choose deploy-llms-with-ansible over distributed-llama when Requirements: Requires Docker; Requires Ansible installed and configured on the local machine.; Debian-based VM with SSH access and Docker must be present.; Tags unique to deploy-llms-with-ansible: ansible, deployment, docker, llama-cpp; When you prefer using Ansible to automate the deployment of LLMs on a Debian-based virtual machine equipped with Docker. ### 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 deploy-llms-with-ansible? When working in an environment that uses alternative automation tools like Terraform or Chef, as this tool specifically requires Ansible knowledge. If the infrastructure does not support or permit the use of Docker for containerizing applications. In cases where extensive customization of models beyond what llama.cpp and Ollama offer is required. ### Is distributed-llama or deploy-llms-with-ansible more popular on GitHub? distributed-llama has more GitHub stars (3,044 vs 3). Stars measure visibility, not whether either tool fits your constraints. ### Are distributed-llama and deploy-llms-with-ansible open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to distributed-llama or deploy-llms-with-ansible? GraphCanon lists graph-backed alternatives at [distributed-llama alternatives](/tools/b4rtaz-distributed-llama/alternatives) and [deploy-llms-with-ansible alternatives](/tools/xamey-deploy-llms-with-ansible/alternatives) ([distributed-llama markdown twin](/tools/b4rtaz-distributed-llama/alternatives.md), [deploy-llms-with-ansible markdown twin](/tools/xamey-deploy-llms-with-ansible/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-xamey-deploy-llms-with-ansible.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, distributed-llama or deploy-llms-with-ansible? distributed-llama: Steady. deploy-llms-with-ansible: Dormant. 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 deploy-llms-with-ansible? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [distributed-llama trust report](/tools/b4rtaz-distributed-llama/trust); [deploy-llms-with-ansible trust report](/tools/xamey-deploy-llms-with-ansible/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/_