--- title: "TensorRT-LLM vs exllama" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nvidia-tensorrt-llm-vs-turboderp-exllama" tools: ["nvidia-tensorrt-llm", "turboderp-exllama"] --- # TensorRT-LLM vs exllama *GraphCanon updated Jul 12, 2026* ## Verdict Pick TensorRT-LLM when license: TensorRT-LLM is Other, exllama is MIT; pick exllama when license: exllama is MIT, TensorRT-LLM is Other. [TensorRT-LLM](https://nvidia.github.io/TensorRT-LLM) reports 14k GitHub stars, 2.5k forks, and 1.5k open issues, last pushed Jul 11, 2026. [exllama](https://github.com/turboderp/exllama) has 2.9k stars, 223 forks, and 65 open issues, last pushed Sep 30, 2023. Figures are from public GitHub metadata via [TensorRT-LLM's repository](https://github.com/NVIDIA/TensorRT-LLM) and [exllama's repository](https://github.com/turboderp/exllama). | | [TensorRT-LLM](/tools/nvidia-tensorrt-llm.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Tagline | Python API for defining and optimizing Large Language Models (LLMs) on NVIDIA GPUs | More memory-efficient rewrite of HF transformers for Llama with quantized weights | | Stars | 14,091 | 2,930 | | Forks | 2,547 | 223 | | Open issues | 1,500 | 65 | | Language | Python | Python | | Adopt for | `TensorRT LLM` is a specialized Python API for optimizing and efficiently running large language models on NVIDIA GPUs, featuring user-friendly interfaces and high-performance optimizations. | - | | Persona | - | - | | Runtime | - | - | | License | Other | MIT | | Categories | LLM Frameworks, Inference & Serving | LLM Frameworks, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [TensorRT-LLM](/tools/nvidia-tensorrt-llm.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 1014d | | Open issues (now) | 1.5k | 65 | | Owner type | Organization | User | | Security scan | 16 low (16 low) | 29 low (29 low) | | Full report | [trust report](/tools/nvidia-tensorrt-llm/trust.md) | [trust report](/tools/turboderp-exllama/trust.md) | ## Decision facts: TensorRT-LLM - **Pricing:** oss - Open source software (OSS) available under a license other than those listed in common OSS categories, implying free use but potentially with restrictions. - **Requirements:** NVIDIA GPU hardware is required for the tool to take full advantage of its optimization capabilities. - **Adopt for:** `TensorRT LLM` is a specialized Python API for optimizing and efficiently running large language models on NVIDIA GPUs, featuring user-friendly interfaces and high-performance optimizations. ## Choose when ### Choose TensorRT-LLM if… - License: TensorRT-LLM is Other, exllama is MIT. - Pricing: Open source software (OSS) available under a license other than those listed in common OSS categories, implying free use but potentially with restrictions.. - Requirements: NVIDIA GPU hardware is required for the tool to take full advantage of its optimization capabilities.. - Tags unique to TensorRT-LLM: moe, cuda, llm-serving, pytorch. - When you are developing or deploying large language models (LLMs) specifically on NVIDIA GPU hardware. ### Choose exllama if… - License: exllama is MIT, TensorRT-LLM is Other. - Tags unique to exllama: nvidia support, gpu optimization, memory efficiency, docker container support. - exllama ships Docker support for self-hosted deployment. ## When NOT to use TensorRT-LLM - When working on CPUs or non-NVIDIA GPUs as the optimizations and hardware support are NVIDIA-specific. - If you prioritize portability across different frameworks over high-performance tuning since TensorRT LLM is tightly integrated with NVIDIA technologies. - For projects that do not require deep level performance optimizations and prefer more general-purpose serving solutions. ## When NOT to use exllama - Last GitHub push was 1015 days ago (dormant maintenance, Sep 30, 2023). Validate activity before betting a new project on exllama. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ## Common questions ### What is the difference between TensorRT-LLM and exllama? TensorRT-LLM: Python API for defining and optimizing Large Language Models (LLMs) on NVIDIA GPUs. exllama: More 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 TensorRT-LLM over exllama? Choose TensorRT-LLM over exllama when License: TensorRT-LLM is Other, exllama is MIT; Pricing: Open source software (OSS) available under a license other than those listed in common OSS categories, implying free use but potentially with restrictions.; Requirements: NVIDIA GPU hardware is required for the tool to take full advantage of its optimization capabilities.; Tags unique to TensorRT-LLM: moe, cuda, llm-serving, pytorch; When you are developing or deploying large language models (LLMs) specifically on NVIDIA GPU hardware. ### When should I choose exllama over TensorRT-LLM? Choose exllama over TensorRT-LLM when License: exllama is MIT, TensorRT-LLM is Other; Tags unique to exllama: nvidia support, gpu optimization, memory efficiency, docker container support; exllama ships Docker support for self-hosted deployment. ### When should I avoid TensorRT-LLM? When working on CPUs or non-NVIDIA GPUs as the optimizations and hardware support are NVIDIA-specific. If you prioritize portability across different frameworks over high-performance tuning since TensorRT LLM is tightly integrated with NVIDIA technologies. For projects that do not require deep level performance optimizations and prefer more general-purpose serving solutions. ### When should I avoid exllama? Last GitHub push was 1015 days ago (dormant maintenance, Sep 30, 2023). Validate activity before betting a new project on exllama. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### Is TensorRT-LLM or exllama more popular on GitHub? TensorRT-LLM has more GitHub stars (14,091 vs 2,930). Stars measure visibility, not whether either tool fits your constraints. ### Are TensorRT-LLM and exllama open source? Yes - both are open-source projects on GitHub (TensorRT-LLM: Other, exllama: MIT). ### Where can I find alternatives to TensorRT-LLM or exllama? GraphCanon lists graph-backed alternatives at [TensorRT-LLM alternatives](/tools/nvidia-tensorrt-llm/alternatives) and [exllama alternatives](/tools/turboderp-exllama/alternatives) ([TensorRT-LLM markdown twin](/tools/nvidia-tensorrt-llm/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-tensorrt-llm-vs-turboderp-exllama.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, TensorRT-LLM or exllama? TensorRT-LLM: Very active. 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 TensorRT-LLM and exllama? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [TensorRT-LLM trust report](/tools/nvidia-tensorrt-llm/trust); [exllama trust report](/tools/turboderp-exllama/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nvidia-tensorrt-llm`](/api/graphcanon/graph?tool=nvidia-tensorrt-llm) - 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/_