--- title: "yalm vs exllama" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/andrewkchan-yalm-vs-turboderp-exllama" tools: ["andrewkchan-yalm", "turboderp-exllama"] --- # yalm vs exllama *GraphCanon updated Aug 25, 2026* ## Verdict Pick yalm if yALM offers a no-frills LLM inference engine in C++/CUDA, optimized for tasks requiring minimal external dependencies beyond I/O and no reliance on heavyweight ML libraries; 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. [yalm](https://github.com/andrewkchan/yalm) reports 596 GitHub stars, 64 forks, and 4 open issues, last pushed Sep 13, 2025. [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 [yalm's repository](https://github.com/andrewkchan/yalm) and [exllama's repository](https://github.com/turboderp/exllama). | | [yalm](/tools/andrewkchan-yalm.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Tagline | LLM inference engine in C++/CUDA without dependency on external libraries except for I/O | Memory-efficient rewrite of HF transformers for Llama with quantized weights | | Stars | 596 | 2,937 | | Forks | 64 | 220 | | Open issues | 4 | 65 | | Language | C++ | Python | | Adopt for | YALM offers a no-frills LLM inference engine in C++/CUDA, optimized for tasks requiring minimal external dependencies beyond I/O and no reliance on heavyweight ML libraries. | 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 | | Categories | Inference & Serving | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [yalm](/tools/andrewkchan-yalm.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 345d | 1041d | | Open issues (now) | 4 | 65 | | Stars delta | +4 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/andrewkchan-yalm/trust.md) | [trust report](/tools/turboderp-exllama/trust.md) | ## Decision facts: yalm - **Adopt for:** YALM offers a no-frills LLM inference engine in C++/CUDA, optimized for tasks requiring minimal external dependencies beyond I/O and no reliance on heavyweight ML libraries. ## 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 yalm if… - yalm is primarily C++; exllama is Python. - Tags unique to yalm: cpp, cuda, llm-inference, machine-learning. - When your project's stack is primarily based on C++ and CUDA, allowing seamless integration without additional dependencies ### Choose exllama if… - exllama is primarily Python; yalm is C++. - 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 yalm - If extensive functionality or ease of use from other ML libraries is required, as YALM does not support dependencies beyond I/O needs - For developers who prefer tools with broader community support and more comprehensive feature sets, given that YALM specializes in a narrow scope ## 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 yalm and exllama? yalm: LLM inference engine in C++/CUDA without dependency on external libraries except for I/O. 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 yalm over exllama? Choose yalm over exllama when yalm is primarily C++; exllama is Python; Tags unique to yalm: cpp, cuda, llm-inference, machine-learning; When your project's stack is primarily based on C++ and CUDA, allowing seamless integration without additional dependencies. ### When should I choose exllama over yalm? Choose exllama over yalm when exllama is primarily Python; yalm is C++; 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 yalm? If extensive functionality or ease of use from other ML libraries is required, as YALM does not support dependencies beyond I/O needs For developers who prefer tools with broader community support and more comprehensive feature sets, given that YALM specializes in a narrow scope ### 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 yalm or exllama more popular on GitHub? exllama has more GitHub stars (2,937 vs 596). Stars measure visibility, not whether either tool fits your constraints. ### Are yalm and exllama open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to yalm or exllama? GraphCanon lists graph-backed alternatives at [yalm alternatives](/tools/andrewkchan-yalm/alternatives) and [exllama alternatives](/tools/turboderp-exllama/alternatives) ([yalm markdown twin](/tools/andrewkchan-yalm/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/andrewkchan-yalm-vs-turboderp-exllama.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, yalm or exllama? yalm: Slowing. 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 yalm and exllama? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [yalm trust report](/tools/andrewkchan-yalm/trust); [exllama trust report](/tools/turboderp-exllama/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=andrewkchan-yalm`](/api/graphcanon/graph?tool=andrewkchan-yalm) - 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/_