--- title: "LLFn vs exllama" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/orgexyz-llfn-vs-turboderp-exllama" tools: ["orgexyz-llfn", "turboderp-exllama"] --- # LLFn vs exllama *GraphCanon updated Sep 20, 2026* ## Verdict Pick LLFn if lightweight, MIT-licensed Python framework for developing with Language Models; 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. [LLFn](https://llfn.orge.xyz/) reports 96 GitHub stars, 7 forks, and 1 open issues, last pushed Jul 30, 2023. [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 [LLFn's repository](https://github.com/orgexyz/LLFn) and [exllama's repository](https://github.com/turboderp/exllama). | | [LLFn](/tools/orgexyz-llfn.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Tagline | A lightweight framework for creating applications using LLMs | Memory-efficient rewrite of HF transformers for Llama with quantized weights | | Stars | 96 | 2,943 | | Forks | 7 | 220 | | Open issues | 1 | 65 | | Language | Python | Python | | Adopt for | Lightweight, MIT-licensed Python framework for developing with Language Models | 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 | MIT | MIT | | Categories | LLM Frameworks | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLFn](/tools/orgexyz-llfn.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Days since push | 1112d | 1072d | | Open issues (now) | 1 | 65 | | Stars delta | 0 (30d) | +6 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/orgexyz-llfn/trust.md) | [trust report](/tools/turboderp-exllama/trust.md) | ## Decision facts: LLFn - **Adopt for:** Lightweight, MIT-licensed Python framework for developing with Language Models ## 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 LLFn if… - Tags unique to LLFn: applications with llms, lightweight. - Ideal for prototyping and small-scale projects needing quick development cycles. - Leaner open-issue backlog (1). ### Choose exllama if… - Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu. - Also covers Inference & Serving. - 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 LLFn - Avoid if requiring extensive customization or large-scale applications with complex scaling needs. - Not recommended for teams prioritizing enterprise-level support and service features. ## 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 LLFn and exllama? LLFn: A lightweight framework for creating applications using LLMs. 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 LLFn over exllama? Choose LLFn over exllama when Tags unique to LLFn: applications with llms, lightweight; Ideal for prototyping and small-scale projects needing quick development cycles; Leaner open-issue backlog (1). ### When should I choose exllama over LLFn? Choose exllama over LLFn when Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu; Also covers Inference & Serving; 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 LLFn? Avoid if requiring extensive customization or large-scale applications with complex scaling needs. Not recommended for teams prioritizing enterprise-level support and service features. ### 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 LLFn or exllama more popular on GitHub? exllama has more GitHub stars (2,943 vs 96). Stars measure visibility, not whether either tool fits your constraints. ### Are LLFn and exllama open source? Yes - both are open-source projects on GitHub (LLFn: MIT, exllama: MIT). ### Where can I find alternatives to LLFn or exllama? GraphCanon lists graph-backed alternatives at [LLFn alternatives](/tools/orgexyz-llfn/alternatives) and [exllama alternatives](/tools/turboderp-exllama/alternatives) ([LLFn markdown twin](/tools/orgexyz-llfn/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/orgexyz-llfn-vs-turboderp-exllama.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LLFn or exllama? LLFn: Dormant. 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 LLFn and exllama? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLFn trust report](/tools/orgexyz-llfn/trust); [exllama trust report](/tools/turboderp-exllama/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=orgexyz-llfn`](/api/graphcanon/graph?tool=orgexyz-llfn) - 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/_