--- title: "distributed-llama vs krasis" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/b4rtaz-distributed-llama-vs-brontoguana-krasis" tools: ["b4rtaz-distributed-llama", "brontoguana-krasis"] --- # distributed-llama vs krasis *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 krasis if krasis is designed to offer efficient large model inference on consumer-grade hardware through hybrid CPU-GPU execution and high-performance optimization. [distributed-llama](https://github.com/b4rtaz/distributed-llama) reports 3.0k GitHub stars, 246 forks, and 48 open issues, last pushed Jul 5, 2026. [krasis](https://github.com/brontoguana/krasis) has 516 stars, 32 forks, and 15 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 [krasis's repository](https://github.com/brontoguana/krasis). | | [distributed-llama](/tools/b4rtaz-distributed-llama.md) | [krasis](/tools/brontoguana-krasis.md) | | --- | --- | --- | | Tagline | Distributed LLM inference using home devices cluster | Hybrid LLM Runtime for Efficient Large Model Inference on Consumer Hardware | | Stars | 3,044 | 516 | | Forks | 246 | 32 | | Open issues | 48 | 15 | | 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. | Krasis is designed to offer efficient large model inference on consumer-grade hardware through hybrid CPU-GPU execution and high-performance optimization. | | Persona | - | - | | Runtime | - | - | | License | MIT | Other | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [distributed-llama](/tools/b4rtaz-distributed-llama.md) | [krasis](/tools/brontoguana-krasis.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 50d | 1d | | Open issues (now) | 48 | 15 | | Open issues delta | 0 (30d) | +7 (30d) | | Full report | [trust report](/tools/b4rtaz-distributed-llama/trust.md) | [trust report](/tools/brontoguana-krasis/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: krasis - **Adopt for:** Krasis is designed to offer efficient large model inference on consumer-grade hardware through hybrid CPU-GPU execution and high-performance optimization. ## Choose when ### Choose distributed-llama if… - License: distributed-llama is MIT, krasis is Other. - 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 krasis if… - License: krasis is Other, distributed-llama is MIT. - Tags unique to krasis: cpu-inference, gguf-model-support, gpu-inference, high-performance-inference. - - When aiming for efficient operation of larger language models with limited VRAM, as Krasis optimizes memory utilization specifically to support this scenario. ## 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 krasis - - Avoid using Krasis if your hardware setup does not include both CPU and GPU capabilities, as its hybrid execution relies on utilizing both components for optimal performance. - - If you prioritize running lightweight models with minimal memory footprint on low-end devices, Krasis might not be the ideal choice given it is optimized for larger-scale model inference. ## Common questions ### What is the difference between distributed-llama and krasis? distributed-llama: Distributed LLM inference using home devices cluster. krasis: Hybrid LLM Runtime for Efficient Large Model Inference on Consumer Hardware. See the comparison table for live GitHub stats and shared categories. ### When should I choose distributed-llama over krasis? Choose distributed-llama over krasis when License: distributed-llama is MIT, krasis is Other; 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 krasis over distributed-llama? Choose krasis over distributed-llama when License: krasis is Other, distributed-llama is MIT; Tags unique to krasis: cpu-inference, gguf-model-support, gpu-inference, high-performance-inference; - When aiming for efficient operation of larger language models with limited VRAM, as Krasis optimizes memory utilization specifically to support this scenario. ### 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 krasis? - Avoid using Krasis if your hardware setup does not include both CPU and GPU capabilities, as its hybrid execution relies on utilizing both components for optimal performance. - If you prioritize running lightweight models with minimal memory footprint on low-end devices, Krasis might not be the ideal choice given it is optimized for larger-scale model inference. ### Is distributed-llama or krasis more popular on GitHub? distributed-llama has more GitHub stars (3,044 vs 516). Stars measure visibility, not whether either tool fits your constraints. ### Are distributed-llama and krasis open source? Yes - both are open-source projects on GitHub (distributed-llama: MIT, krasis: Other). ### Where can I find alternatives to distributed-llama or krasis? GraphCanon lists graph-backed alternatives at [distributed-llama alternatives](/tools/b4rtaz-distributed-llama/alternatives) and [krasis alternatives](/tools/brontoguana-krasis/alternatives) ([distributed-llama markdown twin](/tools/b4rtaz-distributed-llama/alternatives.md), [krasis markdown twin](/tools/brontoguana-krasis/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-brontoguana-krasis.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, distributed-llama or krasis? distributed-llama: Steady. krasis: 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 krasis? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [distributed-llama trust report](/tools/b4rtaz-distributed-llama/trust); [krasis trust report](/tools/brontoguana-krasis/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/_