--- title: "alpaca-lora vs exllama" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/tloen-alpaca-lora-vs-turboderp-exllama" tools: ["tloen-alpaca-lora", "turboderp-exllama"] --- # alpaca-lora vs exllama *GraphCanon updated Aug 7, 2026* ## Verdict Pick alpaca-lora if alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration; 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. [alpaca-lora](https://github.com/tloen/alpaca-lora) reports 19k GitHub stars, 2.2k forks, and 365 open issues, last pushed Jul 29, 2024. [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 [alpaca-lora's repository](https://github.com/tloen/alpaca-lora) and [exllama's repository](https://github.com/turboderp/exllama). | | [alpaca-lora](/tools/tloen-alpaca-lora.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Tagline | Instruct-tune LLaMA on consumer hardware | Memory-efficient rewrite of HF transformers for Llama with quantized weights | | Stars | 18,912 | 2,937 | | Forks | 2,180 | 220 | | Open issues | 365 | 65 | | Language | Jupyter Notebook | Python | | Adopt for | alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration. | 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 | developer harness | - | | Runtime | - | - | | License | The Apache-2.0 license applies, allowing wide-ranging reuse and distribution of the software, provided that copyright notices are included and applicable files accompany distributed executables. | MIT | | Categories | Inference & Serving, LLM Frameworks, Model Training | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [alpaca-lora](/tools/tloen-alpaca-lora.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Days since push | 734d | 1041d | | Open issues (now) | 365 | 65 | | Full report | [trust report](/tools/tloen-alpaca-lora/trust.md) | [trust report](/tools/turboderp-exllama/trust.md) | ## Decision facts: alpaca-lora - **Pricing:** freemium - The source code is freely available under the Apache-2.0 license, but costs associated with hardware and cloud services for running Docker may apply. - **Adopt for:** alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration. - **License detail:** The Apache-2.0 license applies, allowing wide-ranging reuse and distribution of the software, provided that copyright notices are included and applicable files accompany distributed executables. - **Persona:** developer harness ## 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 alpaca-lora if… - alpaca-lora is primarily Jupyter Notebook; exllama is Python. - License: alpaca-lora is Apache-2.0, exllama is MIT. - Pricing: The source code is freely available under the Apache-2.0 license, but costs associated with hardware and cloud services for running Docker may apply.. - Tags unique to alpaca-lora: consumer hardware, instruct-tune, llama, lora. - Also covers Model Training. - When you have limited GPU resources but want to perform instruction-fine-tuning on the LLaMA model, and your setup supports basic Docker. ### Choose exllama if… - exllama is primarily Python; alpaca-lora is Jupyter Notebook. - License: exllama is MIT, alpaca-lora is Apache-2.0. - Tags unique to exllama: llama model, memory-efficient, nvidia gpu, python. - - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support. ## When NOT to use alpaca-lora - When you require more advanced customization beyond what is offered through the `finetune.py` script parameters or Jupyter Notebook interface. - For teams with high-performance computing resources aiming for optimal performance, as alpaca-lora is optimized for use on consumer-grade hardware. ## 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 alpaca-lora and exllama? alpaca-lora: Instruct-tune LLaMA 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 alpaca-lora over exllama? Choose alpaca-lora over exllama when alpaca-lora is primarily Jupyter Notebook; exllama is Python; License: alpaca-lora is Apache-2.0, exllama is MIT; Pricing: The source code is freely available under the Apache-2.0 license, but costs associated with hardware and cloud services for running Docker may apply.; Tags unique to alpaca-lora: consumer hardware, instruct-tune, llama, lora; Also covers Model Training; When you have limited GPU resources but want to perform instruction-fine-tuning on the LLaMA model, and your setup supports basic Docker. ### When should I choose exllama over alpaca-lora? Choose exllama over alpaca-lora when exllama is primarily Python; alpaca-lora is Jupyter Notebook; License: exllama is MIT, alpaca-lora is Apache-2.0; Tags unique to exllama: llama model, memory-efficient, nvidia gpu, python; - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support. ### When should I avoid alpaca-lora? When you require more advanced customization beyond what is offered through the `finetune.py` script parameters or Jupyter Notebook interface. For teams with high-performance computing resources aiming for optimal performance, as alpaca-lora is optimized for use on consumer-grade hardware. ### 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 alpaca-lora or exllama more popular on GitHub? alpaca-lora has more GitHub stars (18,912 vs 2,937). Stars measure visibility, not whether either tool fits your constraints. ### Are alpaca-lora and exllama open source? Yes - both are open-source projects on GitHub (alpaca-lora: Apache-2.0, exllama: MIT). ### Where can I find alternatives to alpaca-lora or exllama? GraphCanon lists graph-backed alternatives at [alpaca-lora alternatives](/tools/tloen-alpaca-lora/alternatives) and [exllama alternatives](/tools/turboderp-exllama/alternatives) ([alpaca-lora markdown twin](/tools/tloen-alpaca-lora/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/tloen-alpaca-lora-vs-turboderp-exllama.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, alpaca-lora or exllama? alpaca-lora: 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 alpaca-lora and exllama? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [alpaca-lora trust report](/tools/tloen-alpaca-lora/trust); [exllama trust report](/tools/turboderp-exllama/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=tloen-alpaca-lora`](/api/graphcanon/graph?tool=tloen-alpaca-lora) - 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/_