airllm vs awesome-llm-apps
A neutral, constraint-first comparison - live GitHub stats and typed relationships, not marketing.
| airllm | awesome-llm-apps | |
|---|---|---|
| Tagline | Repository for running large language models with reduced memory usage on limited GPU hardware. | 100+ AI Agent & RAG apps you can actually run — clone, customize, ship. |
| Stars | 22k | 117k |
| Forks | 2.6k | 17k |
| Open issues | 106 | 6 |
| Language | Jupyter Notebook | Python |
| License | Apache-2.0 | Apache-2.0 |
| Last pushed | Jul 7, 2026 | Jun 15, 2026 |
| Categories | Model Training, Inference & Serving | AI Agents, LLM Frameworks |
airllm
AirLLM allows efficient inference of large language models like 70B parameter sizes using only a single 4GB GPU, without applying techniques such as quantization, distillation, or pruning. It supports various large-scale models and enhances performance capabilities through continuous updates focusing on model optimizations.
Jupyter Notebook
awesome-llm-apps
A repository containing a collection of AI agent and Retrieval-Augmented Generation (RAG) applications that are ready to be cloned, customized, and deployed. The projects cover various aspects such as AI agents, always-on agents, multi-agent teams, RAG techniques, voice agents, fine-tuning for specific use cases, and more.
Python