caveman vs airllm
A neutral, constraint-first comparison - live GitHub stats and typed relationships, not marketing.
| caveman | airllm | |
|---|---|---|
| Tagline | Cuts 65% of tokens in AI coding agent responses. | Repository for running large language models with reduced memory usage on limited GPU hardware. |
| Stars | 86k | 22k |
| Forks | 4.8k | 2.6k |
| Open issues | 370 | 106 |
| Language | JavaScript | Jupyter Notebook |
| License | MIT | Apache-2.0 |
| Last pushed | Jul 3, 2026 | Jul 7, 2026 |
| Categories | LLM Frameworks, Developer Tools | Inference & Serving, Model Training |
caveman
A skill/plugin for various AI agents, including Claude Code and other platforms, reducing output tokens for more concise, direct communication while maintaining accuracy.
JavaScript
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