ExLlamaV2 vs RAGFlow
A side-by-side comparison of two self-hosted self-hosted ai options — licensing, setup difficulty, resource needs, and what each one replaces.
Not the right match-up?
ExLlamaV2
Fast inference library for quantized LLMs on consumer GPUs
VS
RAGFlow
RAG engine with deep document understanding
| Feature | ExLlamaV2 | RAGFlow |
|---|---|---|
| Category | Self-Hosted AI | Self-Hosted AI |
| License | MIT | Apache-2.0 |
| Language | Python | Go |
| Setup difficulty | Hard | Medium |
| Min. RAM | 8,192 MB | 8,192 MB |
| Deployment | bare-metal, source | docker, kubernetes |
| GitHub stars | ★ 4,602 | ★ 87,057 |
| First released | 2023 | 2024 |
| Replaces | OpenAI API | NotebookLM |
Why pick each one
Choose ExLlamaV2 if…
- Released under the MIT license
- Active community (4.6k GitHub stars)
- Written in Python
Choose RAGFlow if…
- Released under the Apache-2.0 license
- First-class Docker support for quick deployment
- Kubernetes-ready with Helm charts available
- Mature project with 87.1k GitHub stars
Frequently asked questions
Is ExLlamaV2 or RAGFlow better?
RAGFlow is the stronger all-round pick: it has both the larger community and the simpler medium setup. Consider ExLlamaV2 if its specific feature set fits your needs better.
Are ExLlamaV2 and RAGFlow free and open-source?
Yes. ExLlamaV2 is licensed under MIT and RAGFlow under Apache-2.0. Both can be self-hosted at no software cost.
Can I run ExLlamaV2 and RAGFlow with Docker?
ExLlamaV2: check the project docs for container support. RAGFlow: yes.