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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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![PyPI - AirLLM
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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. 10% speed improvement.

[2023/

Top issues

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Latest release

Streamed LoRA: frozen weights stay on disk, adapters stay on GPU. Qwen3.8-Flash-Next trains under 6GB (RTX 3060 Ti); Qwen3.8-27B trains in ~2GB at seq 512.

bash
pip install -U airllm
pip install git+https://github.com/huggingface/transformers.git


Point --data at a JSONL file ({"text": "..."} or {"prompt": "...", "completion": "..."}):

bash
python air_llm/examples/train_qwen38_flash_next_lora.py   --data my_data.jsonl   --seq-len 512   --epochs 1   --save-adapter qwen38-flash-next-lora.pt


Flash-Next still needs in-tree qwen4_exp (GitHub transformers main today).