--- title: "krasis vs exllama" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/brontoguana-krasis-vs-turboderp-exllama" tools: ["brontoguana-krasis", "turboderp-exllama"] --- # krasis vs exllama *GraphCanon updated Aug 25, 2026* ## Verdict 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; pick exllama if exLlama provides a memory-efficient implementation of the LLaMa model with support for quantized weights, primarily aimed at users with NVIDIA GPUs from the 30-series onwards. [krasis](https://github.com/brontoguana/krasis) reports 516 GitHub stars, 32 forks, and 15 open issues, last pushed Aug 24, 2026. [exllama](https://github.com/turboderp/exllama) has 2.9k stars, 220 forks, and 65 open issues, last pushed Sep 30, 2023. Figures are from public GitHub metadata via [krasis's repository](https://github.com/brontoguana/krasis) and [exllama's repository](https://github.com/turboderp/exllama). | | [krasis](/tools/brontoguana-krasis.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Tagline | Hybrid LLM Runtime for Efficient Large Model Inference on Consumer Hardware | Memory-efficient rewrite of HF transformers for Llama with quantized weights | | Stars | 516 | 2,937 | | Forks | 32 | 220 | | Open issues | 15 | 65 | | Language | C++ | Python | | Adopt for | Krasis is designed to offer efficient large model inference on consumer-grade hardware through hybrid CPU-GPU execution and high-performance optimization. | ExLlama provides a memory-efficient implementation of the LLaMa model with support for quantized weights, primarily aimed at users with NVIDIA GPUs from the 30-series onwards. | | Persona | - | - | | Runtime | - | - | | License | Other | MIT | | Categories | Inference & Serving | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [krasis](/tools/brontoguana-krasis.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 1d | 1041d | | Open issues (now) | 15 | 65 | | Stars delta | +32 (30d) | Unknown | | Open issues delta | +7 (30d) | Unknown | | Full report | [trust report](/tools/brontoguana-krasis/trust.md) | [trust report](/tools/turboderp-exllama/trust.md) | ## 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. ## Decision facts: exllama - **Adopt for:** ExLlama provides a memory-efficient implementation of the LLaMa model with support for quantized weights, primarily aimed at users with NVIDIA GPUs from the 30-series onwards. ## Choose when ### Choose krasis if… - krasis is primarily C++; exllama is Python. - License: krasis is Other, exllama 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. ### Choose exllama if… - exllama is primarily Python; krasis is C++. - License: exllama is MIT, krasis is Other. - Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu. - Also covers LLM Frameworks. - exllama ships Docker support for self-hosted deployment. - - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support. ## 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. ## When NOT to use exllama - - If you are operating older GPUs such as Pascal series, which lack robust FP16 support; alternatives like AutoGPTQ might perform better. - - In scenarios that involve AMD GPU hardware (due to limited testing and optimization efforts). ## Common questions ### What is the difference between krasis and exllama? krasis: Hybrid LLM Runtime for Efficient Large Model Inference on Consumer Hardware. exllama: Memory-efficient rewrite of HF transformers for Llama with quantized weights. See the comparison table for live GitHub stats and shared categories. ### When should I choose krasis over exllama? Choose krasis over exllama when krasis is primarily C++; exllama is Python; License: krasis is Other, exllama 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 choose exllama over krasis? Choose exllama over krasis when exllama is primarily Python; krasis is C++; License: exllama is MIT, krasis is Other; Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu; Also covers LLM Frameworks; exllama ships Docker support for self-hosted deployment; - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support. ### 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. ### When should I avoid exllama? - If you are operating older GPUs such as Pascal series, which lack robust FP16 support; alternatives like AutoGPTQ might perform better. - In scenarios that involve AMD GPU hardware (due to limited testing and optimization efforts). ### Is krasis or exllama more popular on GitHub? exllama has more GitHub stars (2,937 vs 516). Stars measure visibility, not whether either tool fits your constraints. ### Are krasis and exllama open source? Yes - both are open-source projects on GitHub (krasis: Other, exllama: MIT). ### Where can I find alternatives to krasis or exllama? GraphCanon lists graph-backed alternatives at [krasis alternatives](/tools/brontoguana-krasis/alternatives) and [exllama alternatives](/tools/turboderp-exllama/alternatives) ([krasis markdown twin](/tools/brontoguana-krasis/alternatives.md), [exllama markdown twin](/tools/turboderp-exllama/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/brontoguana-krasis-vs-turboderp-exllama.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, krasis or exllama? krasis: Very active. exllama: 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 krasis and exllama? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [krasis trust report](/tools/brontoguana-krasis/trust); [exllama trust report](/tools/turboderp-exllama/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=brontoguana-krasis`](/api/graphcanon/graph?tool=brontoguana-krasis) - 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/_