In the Crest catalog
lyogavin/airllm
AirLLM 70B inference with single 4GB GPU
- Stars
- 35.3K
- Forks
- 3.7K
- Language
- Jupyter Notebook
- Last push
- 23d ago
- Last release
- 49d ago
Linked by the package’s own registry metadata.GitHub (opens in a new tab)
Package · PyPI
PyPI · v4.0.0 · Apache Software License
AirLLM runs 70B large language models on a single 4GB GPU without quantization, distillation or pruning. Kimi K3 2.8T on under 4GB, Qwen3.8-Flash-Next 125B on 6GB, DeepSeek-V3 671B on ~12GB. Train Qwen3.8-Flash-Next under 6GB.
Tentative categories were assigned with moderate confidence (0.5 to 0.75): they are shown, but they do not count toward a category’s momentum. How categories work
pip install airllmPyPI publishes no adoption figure, so there is no growth to derive. Nothing is estimated in its place.
Adoption history
PyPI publishes no adoption count, so there is no history to chart. That is a fact about the registry, not a gap in ours: the package’s releases and maintenance are measured instead.
In the Crest catalog
AirLLM 70B inference with single 4GB GPU
Linked by the package’s own registry metadata.GitHub (opens in a new tab)
>=1.0>=2.4<6,>=4.49No published news event mentions this package or its repository yet.