Comparison
LLFn vs exllama
Verdict
Pick LLFn if lightweight, MIT-licensed Python framework for developing with Language Models; 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 · LLFn alternatives · exllama alternatives
GraphCanon updated Sep 20, 2026
Trust & integrity
| Signal | LLFn | exllama |
|---|---|---|
| Maintenance | Dormant (1112d since push) As of Aug 16, 2026 · github_public_v1 | Dormant (1072d since push) As of Sep 7, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Aug 16, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 7, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | Published findings As of Jul 11, 2026 · 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
- LLFn
- A lightweight framework for creating applications using LLMs
- exllama
- Memory-efficient rewrite of HF transformers for Llama with quantized weights
Stars
- LLFn
- 96
- exllama
- 2.9k
Forks
- LLFn
- 7
- exllama
- 220
Open issues
- LLFn
- 1
- exllama
- 65
Language
- LLFn
- Python
- exllama
- Python
Adopt for
- LLFn
- Lightweight, MIT-licensed Python framework for developing with Language Models
- 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
- LLFn
- -
- exllama
- -
Runtime
- LLFn
- -
- exllama
- -
License
- LLFn
- MIT
- exllama
- MIT
Last pushed
- LLFn
- Jul 30, 2023
- exllama
- Sep 30, 2023
Categories
- LLFn
- LLM Frameworks
- exllama
- Inference & Serving, LLM Frameworks
Trust and health
Days since push
- LLFn
- 1112d
- exllama
- 1072d
Open issues (now)
- LLFn
- 1
- exllama
- 65
Stars delta
- LLFn
- 0 (30d)
- exllama
- +6 (30d)
Owner type
- LLFn
- Organization
- exllama
- User
OSV dependency advisories
- LLFn
- No lockfile (source not queried)
- exllama
- Published findings
Full report
- LLFn
- Trust report
- exllama
- Trust report
Choose LLFn if…
- Tags unique to LLFn: applications with llms, lightweight.
- Ideal for prototyping and small-scale projects needing quick development cycles.
- Leaner open-issue backlog (1).
When NOT to use LLFn
- Avoid if requiring extensive customization or large-scale applications with complex scaling needs.
- Not recommended for teams prioritizing enterprise-level support and service features.
Choose exllama if…
- Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu.
- Also covers Inference & Serving.
- 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 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 (orgexyz/LLFn) · observed Sep 20, 2026
- GitHub forks (orgexyz/LLFn) · observed Sep 20, 2026
- Last push (orgexyz/LLFn) · observed Jul 30, 2023
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (turboderp/exllama) · observed Sep 20, 2026
- GitHub forks (turboderp/exllama) · observed Sep 20, 2026
- Last push (turboderp/exllama) · observed Sep 30, 2023
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: LLFn 96 · exllama 2.9k (synced Sep 20, 2026).
Common questions
- What is the difference between LLFn and exllama?
- LLFn: A lightweight framework for creating applications using LLMs. 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 LLFn over exllama?
- Choose LLFn over exllama when Tags unique to LLFn: applications with llms, lightweight; Ideal for prototyping and small-scale projects needing quick development cycles; Leaner open-issue backlog (1).
- When should I choose exllama over LLFn?
- Choose exllama over LLFn when Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu; Also covers Inference & Serving; 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 LLFn?
- Avoid if requiring extensive customization or large-scale applications with complex scaling needs. Not recommended for teams prioritizing enterprise-level support and service features.
- 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 LLFn or exllama more popular on GitHub?
- exllama has more GitHub stars (2,943 vs 96). Stars measure visibility, not whether either tool fits your constraints.
- Are LLFn and exllama open source?
- Yes - both are open-source projects on GitHub (LLFn: MIT, exllama: MIT).
- Where can I find alternatives to LLFn or exllama?
- GraphCanon lists graph-backed alternatives at LLFn alternatives and exllama alternatives (LLFn 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, LLFn or exllama?
- LLFn: 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 LLFn and exllama?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLFn trust report; exllama trust report.