Comparison
exllama vs awesome-LLM-resources
Verdict
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; pick awesome-LLM-resources if awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference.
Markdown twin · exllama alternatives · awesome-LLM-resources alternatives
GraphCanon updated Sep 20, 2026
9views this month
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Trust & integrity
| Signal | exllama | awesome-LLM-resources |
|---|---|---|
| Maintenance | Dormant (1072d since push) As of Sep 7, 2026 · github_public_v1 | Very active (3d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 7, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Sep 18, 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
- exllama
- Memory-efficient rewrite of HF transformers for Llama with quantized weights
- awesome-LLM-resources
- Summary of the world's best LLM resources.
Stars
- exllama
- 2.9k
- awesome-LLM-resources
- 9.0k
Forks
- exllama
- 220
- awesome-LLM-resources
- 993
Open issues
- exllama
- 65
- awesome-LLM-resources
- 40
Language
- exllama
- Python
- awesome-LLM-resources
- -
Adopt for
- 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.
- awesome-LLM-resources
- awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference.
Persona
- exllama
- -
- awesome-LLM-resources
- -
Runtime
- exllama
- -
- awesome-LLM-resources
- -
License
- exllama
- MIT
- awesome-LLM-resources
- The repository is licensed under Apache-2.0, allowing for free use, modification, and distribution.
Last pushed
- exllama
- Sep 30, 2023
- awesome-LLM-resources
- Sep 14, 2026
Categories
- exllama
- Inference & Serving, LLM Frameworks
- awesome-LLM-resources
- AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
Trust and health
Maintenance
- exllama
- Dormant (18%)
- awesome-LLM-resources
- Very active (96%)
Days since push
- exllama
- 1072d
- awesome-LLM-resources
- 3d
Open issues (now)
- exllama
- 65
- awesome-LLM-resources
- 40
Stars delta
- exllama
- +6 (30d)
- awesome-LLM-resources
- +123 (30d)
Open issues delta
- exllama
- 0 (30d)
- awesome-LLM-resources
- +17 (30d)
OSV dependency advisories
- exllama
- Published findings
- awesome-LLM-resources
- No lockfile (source not queried)
Full report
- exllama
- Trust report
- awesome-LLM-resources
- Trust report
Choose exllama if…
- License: exllama is MIT, awesome-LLM-resources 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).
Choose awesome-LLM-resources if…
- License: awesome-LLM-resources is Apache-2.0, exllama is MIT.
- Pricing: The repository itself is free to use, but some linked resources may require payment or have associated costs..
- Requirements: The repository does not specify any technical requirements for accessing its content..
- Tags unique to awesome-LLM-resources: awesome-list, book, course, large-language-models.
- Also covers AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Model Training.
- When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more.
When NOT to use awesome-LLM-resources
- If you are looking for a tool that provides direct access to LLM APIs or services, as this repository is a list of resources rather than a service provider.
- When you need real-time support or a community forum for troubleshooting LLM-related issues, as this repository is a static list of resources without interactive support.
Explore
exllama trust report →awesome-LLM-resources trust report →Inference & Serving category →LLM Frameworks category →AI Agents category →Computer Vision category →Data & Retrieval category →Developer Tools category →Evaluation & Observability category →Model Training category →All comparisonsStack workflowsTrending tools
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- 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 (WangRongsheng/awesome-LLM-resources) · observed Sep 20, 2026
- GitHub forks (WangRongsheng/awesome-LLM-resources) · observed Sep 20, 2026
- Last push (WangRongsheng/awesome-LLM-resources) · observed Sep 14, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: exllama 2.9k · awesome-LLM-resources 9.0k (synced Sep 20, 2026).
Common questions
- What is the difference between exllama and awesome-LLM-resources?
- exllama: Memory-efficient rewrite of HF transformers for Llama with quantized weights. awesome-LLM-resources: Summary of the world's best LLM resources.. See the comparison table for live GitHub stats and shared categories.
- When should I choose exllama over awesome-LLM-resources?
- Choose exllama over awesome-LLM-resources when License: exllama is MIT, awesome-LLM-resources 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 choose awesome-LLM-resources over exllama?
- Choose awesome-LLM-resources over exllama when License: awesome-LLM-resources is Apache-2.0, exllama is MIT; Pricing: The repository itself is free to use, but some linked resources may require payment or have associated costs.; Requirements: The repository does not specify any technical requirements for accessing its content.; Tags unique to awesome-LLM-resources: awesome-list, book, course, large-language-models; Also covers AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Model Training; When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more.
- 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).
- When should I avoid awesome-LLM-resources?
- If you are looking for a tool that provides direct access to LLM APIs or services, as this repository is a list of resources rather than a service provider. When you need real-time support or a community forum for troubleshooting LLM-related issues, as this repository is a static list of resources without interactive support.
- Is exllama or awesome-LLM-resources more popular on GitHub?
- awesome-LLM-resources has more GitHub stars (8,968 vs 2,943). Stars measure visibility, not whether either tool fits your constraints.
- Are exllama and awesome-LLM-resources open source?
- Yes - both are open-source projects on GitHub (exllama: MIT, awesome-LLM-resources: Apache-2.0).
- Where can I find alternatives to exllama or awesome-LLM-resources?
- GraphCanon lists graph-backed alternatives at exllama alternatives and awesome-LLM-resources alternatives (exllama markdown twin, awesome-LLM-resources 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, exllama or awesome-LLM-resources?
- exllama: Dormant. awesome-LLM-resources: Very active. 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 exllama and awesome-LLM-resources?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: exllama trust report; awesome-LLM-resources trust report.