Comparison
quant.cpp vs exllama
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.
Markdown twin · quant.cpp alternatives · exllama alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | quant.cpp | exllama |
|---|---|---|
| Maintenance | Slowing (90d since push) As of 1mo · github_public_v1 | Dormant (1041d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1mo · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- quant.cpp
- LLM inference with extended context using C
- exllama
- Memory-efficient rewrite of HF transformers for Llama with quantized weights
Stars
- quant.cpp
- 395
- exllama
- 2.9k
Forks
- quant.cpp
- 42
- exllama
- 220
Open issues
- quant.cpp
- 11
- exllama
- 65
Language
- quant.cpp
- C
- exllama
- Python
Adopt for
- quant.cpp
- quant.cpp, a lossless KV cache compression and quantization tool for LLM inference in pure C without dependencies.
- exllama
- 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
- quant.cpp
- -
- exllama
- -
Runtime
- quant.cpp
- -
- exllama
- -
License
- quant.cpp
- Quant.cpp uses the Apache-2.0 license, which allows for free use, modification, and distribution. Contributions to its codebase are welcomed.
- exllama
- MIT
Last pushed
- quant.cpp
- Apr 26, 2026
- exllama
- Sep 30, 2023
Categories
- quant.cpp
- Inference & Serving
- exllama
- Inference & Serving, LLM Frameworks
Trust and health
Maintenance
- quant.cpp
- Slowing (36%)
- exllama
- Dormant (18%)
Days since push
- quant.cpp
- 90d
- exllama
- 1041d
Open issues (now)
- quant.cpp
- 11
- exllama
- 65
Owner type
- quant.cpp
- Organization
- exllama
- User
OSV dependency advisories
- quant.cpp
- No lockfile (source not queried)
- exllama
- Published findings
Full report
- quant.cpp
- Trust report
- exllama
- Trust report
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.
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.
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 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).
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (quantumaikr/quant.cpp) · observed Jul 26, 2026
- GitHub forks (quantumaikr/quant.cpp) · observed Jul 26, 2026
- Last push (quantumaikr/quant.cpp) · observed Apr 26, 2026
- License file (Apache-2.0) · observed Jul 26, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (turboderp/exllama) · observed Aug 7, 2026
- GitHub forks (turboderp/exllama) · observed Aug 7, 2026
- Last push (turboderp/exllama) · observed Sep 30, 2023
- License file (MIT) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: quant.cpp 395 · exllama 2.9k (synced Jul 26, 2026).
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 395). 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 and exllama alternatives (quant.cpp 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, 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; exllama trust report.