--- title: "Star-Attention vs exllama" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nvidia-star-attention-vs-turboderp-exllama" tools: ["nvidia-star-attention", "turboderp-exllama"] --- # Star-Attention vs exllama *GraphCanon updated Aug 25, 2026* ## Verdict Pick Star-Attention if star-Attention specializes in long sequence inference of large language models using star-attention to maintain efficiency; 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. [Star-Attention](https://arxiv.org/abs/2411.17116) reports 392 GitHub stars, 25 forks, and 1 open issues, last pushed Jun 25, 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 [Star-Attention's repository](https://github.com/NVIDIA/Star-Attention) and [exllama's repository](https://github.com/turboderp/exllama). | | [Star-Attention](/tools/nvidia-star-attention.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Tagline | Efficient LLM Inference over Long Sequences | Memory-efficient rewrite of HF transformers for Llama with quantized weights | | Stars | 392 | 2,937 | | Forks | 25 | 220 | | Open issues | 1 | 65 | | Language | Python | Python | | Adopt for | Star-Attention specializes in long sequence inference of large language models using star-attention to maintain efficiency. | 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 | Apache-2.0 | MIT | | Categories | Inference & Serving | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Star-Attention](/tools/nvidia-star-attention.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Days since push | 425d | 1041d | | Open issues (now) | 1 | 65 | | Stars delta | 0 (30d) | Unknown | | Open issues delta | +1 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/nvidia-star-attention/trust.md) | [trust report](/tools/turboderp-exllama/trust.md) | ## Decision facts: Star-Attention - **Adopt for:** Star-Attention specializes in long sequence inference of large language models using star-attention to maintain efficiency. ## 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 Star-Attention if… - License: Star-Attention is Apache-2.0, exllama is MIT. - Tags unique to Star-Attention: attention-mechanism, large language models, llm-inference. - For applications requiring handling very large input sequences ### Choose exllama if… - License: exllama is MIT, Star-Attention 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 NOT to use Star-Attention - If your use case involves short sequence processing only - In scenarios where traditional attention mechanisms yield adequate results without performance loss ## 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 Star-Attention and exllama? Star-Attention: Efficient LLM Inference over Long Sequences. 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 Star-Attention over exllama? Choose Star-Attention over exllama when License: Star-Attention is Apache-2.0, exllama is MIT; Tags unique to Star-Attention: attention-mechanism, large language models, llm-inference; For applications requiring handling very large input sequences. ### When should I choose exllama over Star-Attention? Choose exllama over Star-Attention when License: exllama is MIT, Star-Attention 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 avoid Star-Attention? If your use case involves short sequence processing only In scenarios where traditional attention mechanisms yield adequate results without performance loss ### 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 Star-Attention or exllama more popular on GitHub? exllama has more GitHub stars (2,937 vs 392). Stars measure visibility, not whether either tool fits your constraints. ### Are Star-Attention and exllama open source? Yes - both are open-source projects on GitHub (Star-Attention: Apache-2.0, exllama: MIT). ### Where can I find alternatives to Star-Attention or exllama? GraphCanon lists graph-backed alternatives at [Star-Attention alternatives](/tools/nvidia-star-attention/alternatives) and [exllama alternatives](/tools/turboderp-exllama/alternatives) ([Star-Attention markdown twin](/tools/nvidia-star-attention/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/nvidia-star-attention-vs-turboderp-exllama.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Star-Attention or exllama? Star-Attention: 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 Star-Attention and exllama? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Star-Attention trust report](/tools/nvidia-star-attention/trust); [exllama trust report](/tools/turboderp-exllama/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nvidia-star-attention`](/api/graphcanon/graph?tool=nvidia-star-attention) - 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/_