--- title: "atlas vs distributed-llama" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/avarok-cybersecurity-atlas-vs-b4rtaz-distributed-llama" tools: ["avarok-cybersecurity-atlas", "b4rtaz-distributed-llama"] --- # atlas vs distributed-llama *GraphCanon updated Aug 25, 2026* ## Verdict Pick atlas if focuses on efficient and scalable model deployment with Rust, supporting various GPU technologies and inference frameworks; 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. [atlas](https://atlasinference.io) reports 667 GitHub stars, 102 forks, and 161 open issues, last pushed Aug 25, 2026. [distributed-llama](https://github.com/b4rtaz/distributed-llama) has 3.0k stars, 246 forks, and 48 open issues, last pushed Jul 5, 2026. Figures are from public GitHub metadata via [atlas's repository](https://github.com/Avarok-Cybersecurity/atlas) and [distributed-llama's repository](https://github.com/b4rtaz/distributed-llama). | | [atlas](/tools/avarok-cybersecurity-atlas.md) | [distributed-llama](/tools/b4rtaz-distributed-llama.md) | | --- | --- | --- | | Tagline | Pure Rust Inference Engine | Distributed LLM inference using home devices cluster | | Stars | 667 | 3,044 | | Forks | 102 | 246 | | Open issues | 161 | 48 | | Language | Rust | C++ | | Adopt for | Focuses on efficient and scalable model deployment with Rust, supporting various GPU technologies and inference frameworks. | distributed-llama is a C++ framework that leverages multiple home devices for faster large language model inference, under the MIT license. | | Persona | - | - | | Runtime | - | - | | License | AGPL-3.0 | MIT | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [atlas](/tools/avarok-cybersecurity-atlas.md) | [distributed-llama](/tools/b4rtaz-distributed-llama.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 50d | | Open issues (now) | 161 | 48 | | Stars delta | +57 (30d) | +32 (30d) | | Open issues delta | +92 (30d) | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/avarok-cybersecurity-atlas/trust.md) | [trust report](/tools/b4rtaz-distributed-llama/trust.md) | ## Decision facts: atlas - **Adopt for:** Focuses on efficient and scalable model deployment with Rust, supporting various GPU technologies and inference frameworks. ## 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. ## Choose when ### Choose atlas if… - atlas is primarily Rust; distributed-llama is C++. - License: atlas is AGPL-3.0, distributed-llama is MIT. - Tags unique to atlas: cuda, dgx, dgx-spark, gb10. - When aiming for high-performance Rust-based deployment that leverages hardware accelerators like NVIDIA DGX systems and Cuda technology. ### Choose distributed-llama if… - distributed-llama is primarily C++; atlas is Rust. - License: distributed-llama is MIT, atlas is AGPL-3.0. - 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 NOT to use atlas - Avoid if you prefer tools in languages other than Rust for inference engines, since this is purely designed in Rust. - Not ideal if your deployment environment does not support NVIDIA GPU technologies such as DGX, which are key to maximize performance with Atlas. ## 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. ## Common questions ### What is the difference between atlas and distributed-llama? atlas: Pure Rust Inference Engine. distributed-llama: Distributed LLM inference using home devices cluster. See the comparison table for live GitHub stats and shared categories. ### When should I choose atlas over distributed-llama? Choose atlas over distributed-llama when atlas is primarily Rust; distributed-llama is C++; License: atlas is AGPL-3.0, distributed-llama is MIT; Tags unique to atlas: cuda, dgx, dgx-spark, gb10; When aiming for high-performance Rust-based deployment that leverages hardware accelerators like NVIDIA DGX systems and Cuda technology. ### When should I choose distributed-llama over atlas? Choose distributed-llama over atlas when distributed-llama is primarily C++; atlas is Rust; License: distributed-llama is MIT, atlas is AGPL-3.0; 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 avoid atlas? Avoid if you prefer tools in languages other than Rust for inference engines, since this is purely designed in Rust. Not ideal if your deployment environment does not support NVIDIA GPU technologies such as DGX, which are key to maximize performance with Atlas. ### 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. ### Is atlas or distributed-llama more popular on GitHub? distributed-llama has more GitHub stars (3,044 vs 667). Stars measure visibility, not whether either tool fits your constraints. ### Are atlas and distributed-llama open source? Yes - both are open-source projects on GitHub (atlas: AGPL-3.0, distributed-llama: MIT). ### Where can I find alternatives to atlas or distributed-llama? GraphCanon lists graph-backed alternatives at [atlas alternatives](/tools/avarok-cybersecurity-atlas/alternatives) and [distributed-llama alternatives](/tools/b4rtaz-distributed-llama/alternatives) ([atlas markdown twin](/tools/avarok-cybersecurity-atlas/alternatives.md), [distributed-llama markdown twin](/tools/b4rtaz-distributed-llama/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/avarok-cybersecurity-atlas-vs-b4rtaz-distributed-llama.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, atlas or distributed-llama? atlas: Very active. distributed-llama: Steady. 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 atlas and distributed-llama? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [atlas trust report](/tools/avarok-cybersecurity-atlas/trust); [distributed-llama trust report](/tools/b4rtaz-distributed-llama/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=avarok-cybersecurity-atlas`](/api/graphcanon/graph?tool=avarok-cybersecurity-atlas) - 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/_