--- title: "llamafile vs exllama" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/mozilla-ai-llamafile-vs-turboderp-exllama" tools: ["mozilla-ai-llamafile", "turboderp-exllama"] --- # llamafile vs exllama *GraphCanon updated Aug 7, 2026* ## Verdict Pick llamafile if leverage llamafile for simplified deployment of language models across different platforms with its single file executable approach; 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. [llamafile](https://docs.mozilla.ai/llamafile) reports 25k GitHub stars, 1.5k forks, and 212 open issues, last pushed Jul 27, 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 [llamafile's repository](https://github.com/mozilla-ai/llamafile) and [exllama's repository](https://github.com/turboderp/exllama). | | [llamafile](/tools/mozilla-ai-llamafile.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Tagline | Distribute and run LLMs with a single file. | Memory-efficient rewrite of HF transformers for Llama with quantized weights | | Stars | 25,470 | 2,937 | | Forks | 1,530 | 220 | | Open issues | 212 | 65 | | Language | C++ | Python | | Adopt for | Leverage llamafile for simplified deployment of language models across different platforms with its single file executable approach. | 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, LLM Frameworks | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llamafile](/tools/mozilla-ai-llamafile.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 2d | 1041d | | Open issues (now) | 212 | 65 | | Owner type | Organization | User | | Full report | [trust report](/tools/mozilla-ai-llamafile/trust.md) | [trust report](/tools/turboderp-exllama/trust.md) | ## Decision facts: llamafile - **Adopt for:** Leverage llamafile for simplified deployment of language models across different platforms with its single file executable approach. ## 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 llamafile if… - llamafile is primarily C++; exllama is Python. - License: llamafile is Other, exllama is MIT. - Tags unique to llamafile: cross-platform, gguf, llama-cpp, local-ai. - - You require cross-platform support for your LLM deployments and prefer managing them via a single file. ### Choose exllama if… - exllama is primarily Python; llamafile is C++. - License: exllama is MIT, llamafile is Other. - Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu. - 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 llamafile - - If you need extensive customization options that cannot be handled through a single file approach. - - In environments where dependency management outside of the single executable is mandatory. ## 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 llamafile and exllama? llamafile: Distribute and run LLMs with a single file.. 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 llamafile over exllama? Choose llamafile over exllama when llamafile is primarily C++; exllama is Python; License: llamafile is Other, exllama is MIT; Tags unique to llamafile: cross-platform, gguf, llama-cpp, local-ai; - You require cross-platform support for your LLM deployments and prefer managing them via a single file. ### When should I choose exllama over llamafile? Choose exllama over llamafile when exllama is primarily Python; llamafile is C++; License: exllama is MIT, llamafile is Other; Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu; 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 llamafile? - If you need extensive customization options that cannot be handled through a single file approach. - In environments where dependency management outside of the single executable is mandatory. ### 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 llamafile or exllama more popular on GitHub? llamafile has more GitHub stars (25,470 vs 2,937). Stars measure visibility, not whether either tool fits your constraints. ### Are llamafile and exllama open source? Yes - both are open-source projects on GitHub (llamafile: Other, exllama: MIT). ### Where can I find alternatives to llamafile or exllama? GraphCanon lists graph-backed alternatives at [llamafile alternatives](/tools/mozilla-ai-llamafile/alternatives) and [exllama alternatives](/tools/turboderp-exllama/alternatives) ([llamafile markdown twin](/tools/mozilla-ai-llamafile/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/mozilla-ai-llamafile-vs-turboderp-exllama.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llamafile or exllama? llamafile: 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 llamafile and exllama? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llamafile trust report](/tools/mozilla-ai-llamafile/trust); [exllama trust report](/tools/turboderp-exllama/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=mozilla-ai-llamafile`](/api/graphcanon/graph?tool=mozilla-ai-llamafile) - 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/_