--- title: "quant.cpp vs exllama" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/quantumaikr-quant-cpp-vs-turboderp-exllama" tools: ["quantumaikr-quant-cpp", "turboderp-exllama"] --- # quant.cpp vs exllama *GraphCanon updated Aug 25, 2026* ## Verdict Pick quant.cpp if quant.cpp, a lossless KV cache compression and quantization tool for LLM inference in pure C without dependencies; 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. [quant.cpp](https://github.com/quantumaikr/quant.cpp) reports 399 GitHub stars, 44 forks, and 11 open issues, last pushed Apr 26, 2026. [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 [quant.cpp's repository](https://github.com/quantumaikr/quant.cpp) and [exllama's repository](https://github.com/turboderp/exllama). | | [quant.cpp](/tools/quantumaikr-quant-cpp.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Tagline | LLM inference with extended context using C | Memory-efficient rewrite of HF transformers for Llama with quantized weights | | Stars | 399 | 2,937 | | Forks | 44 | 220 | | Open issues | 11 | 65 | | Language | C | Python | | Adopt for | quant.cpp, a lossless KV cache compression and quantization tool for LLM inference in pure C without dependencies. | 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 | Quant.cpp uses the Apache-2.0 license, which allows for free use, modification, and distribution. Contributions to its codebase are welcomed. | MIT | | Categories | Inference & Serving | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [quant.cpp](/tools/quantumaikr-quant-cpp.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 121d | 1041d | | Open issues (now) | 11 | 65 | | Stars delta | +4 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/quantumaikr-quant-cpp/trust.md) | [trust report](/tools/turboderp-exllama/trust.md) | ## Decision facts: quant.cpp - **Requirements:** Requires a C compiler compatible with quant.cpp source code. - **Adopt for:** quant.cpp, a lossless KV cache compression and quantization tool for LLM inference in pure C without dependencies. - **License detail:** Quant.cpp uses the Apache-2.0 license, which allows for free use, modification, and distribution. Contributions to its codebase are welcomed. ## 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 quant.cpp if… - quant.cpp is primarily C; exllama is Python. - License: quant.cpp is Apache-2.0, exllama is MIT. - Requirements: Requires a C compiler compatible with quant.cpp source code.. - Tags unique to quant.cpp: delta-compression, embeddable, gguf, kv-cache. - Use quant.cpp when you need extended context for LLM inference in a lightweight, embeddable environment with no external dependencies. ### Choose exllama if… - exllama is primarily Python; quant.cpp is C. - License: exllama is MIT, quant.cpp is Apache-2.0. - Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu. - Also covers LLM Frameworks. - - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support. ## When NOT to use quant.cpp - Avoid using quant.cpp for projects requiring non-C language support or frameworks since it strictly operates within the context of pure C. - Do not use quant.cpp in environments where rapid runtime performance is paramount and additional compile-time overhead introduced by its unique compression techniques may cause delays. ## 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 quant.cpp and exllama? quant.cpp: LLM inference with extended context using C. 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 quant.cpp over exllama? Choose quant.cpp over exllama when quant.cpp is primarily C; exllama is Python; License: quant.cpp is Apache-2.0, exllama is MIT; Requirements: Requires a C compiler compatible with quant.cpp source code.; Tags unique to quant.cpp: delta-compression, embeddable, gguf, kv-cache; Use quant.cpp when you need extended context for LLM inference in a lightweight, embeddable environment with no external dependencies. ### When should I choose exllama over quant.cpp? Choose exllama over quant.cpp when exllama is primarily Python; quant.cpp is C; License: exllama is MIT, quant.cpp is Apache-2.0; Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu; Also covers LLM Frameworks; - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support. ### When should I avoid quant.cpp? Avoid using quant.cpp for projects requiring non-C language support or frameworks since it strictly operates within the context of pure C. Do not use quant.cpp in environments where rapid runtime performance is paramount and additional compile-time overhead introduced by its unique compression techniques may cause delays. ### 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 quant.cpp or exllama more popular on GitHub? exllama has more GitHub stars (2,937 vs 399). Stars measure visibility, not whether either tool fits your constraints. ### Are quant.cpp and exllama open source? Yes - both are open-source projects on GitHub (quant.cpp: Apache-2.0, exllama: MIT). ### Where can I find alternatives to quant.cpp or exllama? GraphCanon lists graph-backed alternatives at [quant.cpp alternatives](/tools/quantumaikr-quant-cpp/alternatives) and [exllama alternatives](/tools/turboderp-exllama/alternatives) ([quant.cpp markdown twin](/tools/quantumaikr-quant-cpp/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/quantumaikr-quant-cpp-vs-turboderp-exllama.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, quant.cpp or exllama? quant.cpp: 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 quant.cpp and exllama? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [quant.cpp trust report](/tools/quantumaikr-quant-cpp/trust); [exllama trust report](/tools/turboderp-exllama/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=quantumaikr-quant-cpp`](/api/graphcanon/graph?tool=quantumaikr-quant-cpp) - 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/_