TensorRT-LLM vs exllama

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 and exllama alternatives (TensorRT-LLM markdown twin, exllama markdown twin), 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 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; exllama trust report.