lyogavin / airllm

**Quickstart** | 
**Configurations** | 
**MacOS** | 
**Example notebooks** | 
**FAQ**
**AirLLM** dramatically reduces inference memory usage, letting 70B large language models run on a single 4GB GPU card — without quantization, distillation, or pruning. You can even run **Kimi K3 (2.8T)** — the largest open-source model released to date — on **under 4GB**, **Qwen3.8-Flash-Next (125B)** on **6GB**, and **DeepSeek-V3 (671B)** on **~12GB**. We now also support training huge models on small VRAM: **Qwen3.8-Flash-Next (125B)** under **6GB**.
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## Updates
[2026/09] **Training** support: stream frozen weights one layer at a time and keep adapters on the GPU. **Qwen3.8-Flash-Next (125B)** trains under **6GB** (RTX 3060 Ti); **Qwen3.8-27B** trains in **~2GB** at seq 512. See Training.
[2026/08] **Qwen3.8-Flash-Next** support: Qwen's 125B MoE flagship (`Qwen4ExpForConditionalGeneration`) with a ~51B n-gram embedding runs in **5.95GB** of VRAM, measured end to end on one RTX 4090. The n-gram table is file-mapped on the host (a 64GB machine is enough); decoder layers stream. Needs a `transformers` build with in-tree `qwen4_exp` (`pip install git+https://github.com/huggingface/transformers.git` today) and ~360GB of checkpoint disk (`delete_original=True` reclaims the originals after the split).
[2026/08] **Qwen3.8-27B** support: Qwen's new dense VL (Gated DeltaNet + Gated Attention, native vision) runs in **3.33GB** of VRAM, measured end to end on one RTX 3090. Needs `transformers` 5.8+.
[2026/07] **Kimi K3 (2.8T)** support: the largest open-source model runs on a single card in **3.72GB** of VRAM, measured end to end on one RTX 6000 Ada. Per-expert streaming loads only the experts a token actually routes to. K3 brings three requirements of its own: `pip install compressed-tensors flash-attn` (its model code mandates flash attention regardless of what you request), a CUDA 12 build of torch, since no prebuilt flash-attn wheel exists for CUDA 13 yet, and `transformers` 4.56.x, as its remote code does not load on 5.x.
[2026/06] **v3.0**: FP8 model support + the latest models. Run **DeepSeek-V3 (671B) on ~12GB** and **Qwen3-235B on ~3GB**, plus Qwen3, Llama 3.x/4, DeepSeek V2/V3, Phi-4, Gemma and more — all through a single `AutoModel`.
[2024/08/20] v2.11.0: Support Qwen2.5
[2024/08/18] v2.10.1 Support CPU inference. Support non sharded models. Thanks @NavodPeiris for the great work! 
[2024/07/30] Support Llama3.1 **405B** (example notebook). Support **8bit/4bit quantization**.
[2024/04/20] AirLLM supports Llama3 natively already. Run Llama3 70B on 4GB single GPU.
[2023/12/25] v2.8.2: Support MacOS running 70B large language models.
[2023/12/20] v2.7: Support AirLLMMixtral. 
[2023/12/20] v2.6: Added AutoModel, automatically detect model type, no need to provide model class to initialize model.
[2023/12/18] v2.5: added prefetching to overlap the model loading and compute