Comparison
litgpt vs exllama
Verdict
Pick litgpt if litGPT offers extensive support for high-performance LLMs with comprehensive workflows for pretraining, fine-tuning, and deployment; 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 · litgpt alternatives · exllama alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | litgpt | exllama |
|---|---|---|
| Maintenance | Active (17d since push) As of 1w · github_public_v1 | Dormant (1041d since push) As of 1w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1w · github_public_v1 | Not a fork · Personal account As of 1w · 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
- litgpt
- High-performance LLMs with recipes for pretraining, finetuning and deployment
- exllama
- Memory-efficient rewrite of HF transformers for Llama with quantized weights
Stars
- litgpt
- 14k
- exllama
- 2.9k
Forks
- litgpt
- 1.5k
- exllama
- 220
Open issues
- litgpt
- 272
- exllama
- 65
Language
- litgpt
- Python
- exllama
- Python
Adopt for
- litgpt
- LitGPT offers extensive support for high-performance LLMs with comprehensive workflows for pretraining, fine-tuning, and deployment.
- 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
- litgpt
- -
- exllama
- -
Runtime
- litgpt
- -
- exllama
- -
License
- litgpt
- LitGPT operates under the open-source Apache-2.0 license, providing permissive terms for use and modification.
- exllama
- MIT
Last pushed
- litgpt
- Jul 20, 2026
- exllama
- Sep 30, 2023
Categories
- litgpt
- Inference & Serving, LLM Frameworks, Model Training
- exllama
- Inference & Serving, LLM Frameworks
Trust and health
Maintenance
- litgpt
- Active (82%)
- exllama
- Dormant (18%)
Days since push
- litgpt
- 17d
- exllama
- 1041d
Open issues (now)
- litgpt
- 272
- exllama
- 65
Stars delta
- litgpt
- +137 (30d)
- exllama
- Unknown
Open issues delta
- litgpt
- +6 (30d)
- exllama
- Unknown
Owner type
- litgpt
- Organization
- exllama
- User
OSV dependency advisories
- litgpt
- No lockfile (source not queried)
- exllama
- Published findings
Full report
- litgpt
- Trust report
- exllama
- Trust report
Choose litgpt if…
- License: litgpt is Apache-2.0, exllama is MIT.
- Pricing: The core LitGPT framework is free to use under an open source license, but users might encounter costs when deploying at scale or using high-performance models..
- Requirements: Min 16 GB RAM.
- Tags unique to litgpt: ai, artificial-intelligence, deep-learning, large language models.
- Also covers Model Training.
- If you are focusing on a project that requires rapid prototyping or experimentation with over 20 different LLMs to find the best fit for your application.
When NOT to use litgpt
- If you need a tool specifically optimized for resource-constrained devices, as LitGPT focuses on high-performance LLMs and may require more resources.
- When your project is strictly limited to only one or two types of specific LLMs; in this case, another specialized framework that caters narrowly might be preferable.
Choose exllama if…
- License: exllama is MIT, litgpt is Apache-2.0.
- Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu.
- 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 (Lightning-AI/litgpt) · observed Aug 7, 2026
- GitHub forks (Lightning-AI/litgpt) · observed Aug 7, 2026
- Last push (Lightning-AI/litgpt) · observed Jul 20, 2026
- License file (Apache-2.0) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 11, 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: litgpt 14k · exllama 2.9k (synced Aug 7, 2026).
Common questions
- What is the difference between litgpt and exllama?
- litgpt: High-performance LLMs with recipes for pretraining, finetuning and deployment. 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 litgpt over exllama?
- Choose litgpt over exllama when License: litgpt is Apache-2.0, exllama is MIT; Pricing: The core LitGPT framework is free to use under an open source license, but users might encounter costs when deploying at scale or using high-performance models.; Requirements: Min 16 GB RAM; Tags unique to litgpt: ai, artificial-intelligence, deep-learning, large language models; Also covers Model Training; If you are focusing on a project that requires rapid prototyping or experimentation with over 20 different LLMs to find the best fit for your application.
- When should I choose exllama over litgpt?
- Choose exllama over litgpt when License: exllama is MIT, litgpt is Apache-2.0; Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu; 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 litgpt?
- If you need a tool specifically optimized for resource-constrained devices, as LitGPT focuses on high-performance LLMs and may require more resources. When your project is strictly limited to only one or two types of specific LLMs; in this case, another specialized framework that caters narrowly might be preferable.
- 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 litgpt or exllama more popular on GitHub?
- litgpt has more GitHub stars (13,605 vs 2,937). Stars measure visibility, not whether either tool fits your constraints.
- Are litgpt and exllama open source?
- Yes - both are open-source projects on GitHub (litgpt: Apache-2.0, exllama: MIT).
- Where can I find alternatives to litgpt or exllama?
- GraphCanon lists graph-backed alternatives at litgpt alternatives and exllama alternatives (litgpt 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, litgpt or exllama?
- litgpt: 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 litgpt and exllama?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: litgpt trust report; exllama trust report.