--- title: "exllama vs xllm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/turboderp-exllama-vs-xllm-ai-xllm" tools: ["turboderp-exllama", "xllm-ai-xllm"] --- # exllama vs xllm *GraphCanon updated Sep 20, 2026* ## Verdict 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; pick xllm if a high-performance inference engine for LLM, VLM, DiT, and REC models by the OpenAtom Foundation. [exllama](https://github.com/turboderp/exllama) reports 2.9k GitHub stars, 220 forks, and 65 open issues, last pushed Sep 30, 2023. [xllm](https://xllm-ai.com/) has 1.6k stars, 300 forks, and 211 open issues, last pushed Sep 19, 2026. Figures are from public GitHub metadata via [exllama's repository](https://github.com/turboderp/exllama) and [xllm's repository](https://github.com/xLLM-AI/xllm). | | [exllama](/tools/turboderp-exllama.md) | [xllm](/tools/xllm-ai-xllm.md) | | --- | --- | --- | | Tagline | Memory-efficient rewrite of HF transformers for Llama with quantized weights | A high-performance inference engine for LLM, VLM, DiT and REC models | | Stars | 2,943 | 1,577 | | Forks | 220 | 300 | | Open issues | 65 | 211 | | Language | Python | C++ | | 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. | A high-performance inference engine for LLM, VLM, DiT, and REC models by the OpenAtom Foundation. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Inference & Serving, LLM Frameworks | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [exllama](/tools/turboderp-exllama.md) | [xllm](/tools/xllm-ai-xllm.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 1072d | 0d | | Open issues (now) | 65 | 211 | | Stars delta | +6 (30d) | +84 (30d) | | Open issues delta | 0 (30d) | +20 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/turboderp-exllama/trust.md) | [trust report](/tools/xllm-ai-xllm/trust.md) | ## 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. ## Decision facts: xllm - **Adopt for:** A high-performance inference engine for LLM, VLM, DiT, and REC models by the OpenAtom Foundation. ## Choose when ### Choose exllama if… - exllama is primarily Python; xllm is C++. - License: exllama is MIT, xllm is Apache-2.0. - 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. ### Choose xllm if… - xllm is primarily C++; exllama is Python. - License: xllm is Apache-2.0, exllama is MIT. - Tags unique to xllm: deepseek, glm, llm-inference. - When developing applications that require optimized performance on various AI accelerators ## 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). ## When NOT to use xllm - If your project strictly requires Python-based inference engines for backend support - In cases preferring proprietary licenses over the Apache-2.0 open-source framework used here ## Common questions ### What is the difference between exllama and xllm? exllama: Memory-efficient rewrite of HF transformers for Llama with quantized weights. xllm: A high-performance inference engine for LLM, VLM, DiT and REC models. See the comparison table for live GitHub stats and shared categories. ### When should I choose exllama over xllm? Choose exllama over xllm when exllama is primarily Python; xllm is C++; License: exllama is MIT, xllm is Apache-2.0; 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 choose xllm over exllama? Choose xllm over exllama when xllm is primarily C++; exllama is Python; License: xllm is Apache-2.0, exllama is MIT; Tags unique to xllm: deepseek, glm, llm-inference; When developing applications that require optimized performance on various AI accelerators. ### 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). ### When should I avoid xllm? If your project strictly requires Python-based inference engines for backend support In cases preferring proprietary licenses over the Apache-2.0 open-source framework used here ### Is exllama or xllm more popular on GitHub? exllama has more GitHub stars (2,943 vs 1,577). Stars measure visibility, not whether either tool fits your constraints. ### Are exllama and xllm open source? Yes - both are open-source projects on GitHub (exllama: MIT, xllm: Apache-2.0). ### Where can I find alternatives to exllama or xllm? GraphCanon lists graph-backed alternatives at [exllama alternatives](/tools/turboderp-exllama/alternatives) and [xllm alternatives](/tools/xllm-ai-xllm/alternatives) ([exllama markdown twin](/tools/turboderp-exllama/alternatives.md), [xllm markdown twin](/tools/xllm-ai-xllm/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/turboderp-exllama-vs-xllm-ai-xllm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, exllama or xllm? exllama: Dormant. xllm: Very active. 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 exllama and xllm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [exllama trust report](/tools/turboderp-exllama/trust); [xllm trust report](/tools/xllm-ai-xllm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=turboderp-exllama`](/api/graphcanon/graph?tool=turboderp-exllama) - 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/_