GitHub - lyogavin/airllm: AirLLM 70B inference with single 4GB GPU

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 405B Llama 3.1 on 8GB, DeepSeek-V3 (671B) on ~12GB, and Kimi K3 (2.8T) — the largest open-source model released to date — on under 4GB, because sparse MoE models stream one expert at a time rather than a whole layer. AI Agents Recommendation: Best AI Game Sprite Generator Best AI Facial Expression Editor Bloome — build & run AI agent teams in the cloud, zero setup Updates [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.

[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. [2023/12/03] added support of ChatGLM, QWen, Baichuan, Mistral, InternLM! [2023/12/02] added support for safetensors. Now support all top 10 models in open llm leaderboard. [2023/12/01] airllm 2.0. Support compressions: 3x run time speed up! [2023/11/20] airllm Initial version! Star History Table of Contents Quick start Model Compression Configurations Run on MacOS Example notebooks Supported Models Acknowledgement FAQ Quickstart 1. Install package First, install the airllm pip package. pip install airllm 2. Inference Then, initialize AirLLMLlama2, pass in the huggingface repo ID of the model being used, or the local path, and inference can be performed similar to a regular transformer model. (You can also specify the path to save the splitted layered model through layer_shards_saving_path when init AirLLMLlama2. from airllm import AutoModel MAX_LENGTH = 128 # just pass a hugging face repo id — works with almost any popular model: model = AutoModel.from_pretrained("Qwen/Qwen3-32B") # go bigger with the exact same one line: #model = AutoModel.from_pretrained("Qwen/Qwen3-235B-A22B") # 235B, runs in ~3GB #model = AutoModel.from_pretrained("deepseek-ai/DeepSeek-V3") # 671B, runs in ~12GB # or use a model's local path... #model = AutoModel.from_pretrained("/home/ubuntu/.cache/huggingface/hub/models--Qwen--Qwen3-32B/snapshots/...") input_text = [ 'What is the capital of United States?', #'I like', ] input_tokens = model.tokenizer(input_text, return_tensors="pt", return_attention_mask=False, truncation=True, max_length=MAX_LENGTH, padding=False) generation_output = model.generate( input_tokens['input_ids'].cuda(), max_new_tokens=20, use_cache=True, return_dict_in_generate=True) output = model.tokenizer.decode(generation_output.sequences[0]) print(output) Note: During inference, the original model will first be decomposed and saved layer-wise. Please ensure there is sufficient disk space in the huggingface cache directory. Model Compression - 3x Inference Speed Up! We just added model compression based on block-wise quantization-based model compression. Which can further speed up the inference speed for up to 3x , with almost ignorable accuracy loss! (see more performance evaluation and why we use block-wise quantization in this paper) How to enable model compression speed up: Step 1. make sure you have bitsandbytes installed by pip install -U bitsandbytes Step 2. make sure airllm verion later than 2.0.0: pip install -U airllm Step 3. when initialize the model, passing the argument compression ('4bit' or '8bit'): model = AutoModel.from_pretrained("garage-bAInd/Platypus2-70B-instruct", compression='4bit' # specify '8bit' for 8-bit block-wise quantization ) What are the differences between model compression and quantization? Quantization normally needs to quantize both weights and activations to really speed things up. Which makes it harder to maintain accuracy and avoid the impact of outliers in all kinds of inputs. While in our case the bottleneck is mainly at the disk loading, we only need to make the model loading size smaller. So, we get to only quantize the weights' part, which is easier to ensure the accuracy. Configurations When initialize the model, we support the following configurations: compression: supported options: 4bit, 8bit for 4-bit or 8-bit block-wise quantization, or by default None for no compression profiling_mode: supported options: True to output time consumptions or by default False layer_shards_saving_path: optionally another path to save the splitted model hf_token: huggingface token can be provided here if downloading gated models like: meta-llama/Llama-2-7b-hf prefetching: prefetching to overlap the model loading and compute. By default, turned on. For now, only AirLLMLlama2 supports this. delete_original: if you don't have too much disk space, you can set delete_original to true to delete the original downloaded hugging face model, only keep the transformed one to save half of the disk space. MacOS Just install airllm and run the code the same as on linux. See more in Quick Start. make sure you installed mlx and torch you probably need to install python native see more here only Apple silicon is supported Example [python notebook] (https://github.com/lyogavin/airllm/blob/main/air_llm/examples/run_on_macos.ipynb) Example Python Notebook Example colabs here: example of other models (ChatGLM, QWen, Baichuan, Mistral, etc): Details ChatGLM: from airllm import AutoModel MAX_LENGTH = 128 model = AutoModel.from_pretrained("THUDM/chatglm3-6b-base") input_text = ['What is the capital of China?',] input_tokens = model.tokenizer(input_text, return_tensors="pt", return_attention_mask=False, truncation=True, max_length=MAX_LENGTH, padding=True) generation_output = model.generate( input_tokens['input_ids'].cuda(), max_new_tokens=5, use_cache= True, return_dict_in_generate=True) model.tokenizer.decode(generation_output.sequences[0]) QWen: from airllm import AutoModel MAX_LENGTH = 128 model = AutoModel.from_pretrained("Qwen/Qwen-7B") input_text = ['What is the capital of China?',] input_tokens = model.tokenizer(input_text, return_tensors="pt", return_attention_mask=False, truncation=True, max_length=MAX_LENGTH) generation_output = model.generate( input_tokens['input_ids'].cuda(), max_new_tokens=5, use_cache=True, return_dict_in_generate=True) model.tokenizer.decode(generation_output.sequences[0]) Baichuan, InternLM, Mistral, etc: from airllm import AutoModel MAX_LENGTH = 128 model = AutoModel.from_pretrained("baichuan-inc/Baichuan2-7B-Base") #model = AutoModel.from_pretrained("internlm/internlm-20b") #model = AutoModel.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") input_text = ['What is the capital of China?',] input_tokens = model.tokenizer(input_text, return_tensors="pt", return_attention_mask=False, truncation=True, max_length=MAX_LENGTH) generation_output = model.generate( input_tokens['input_ids'].cuda(), max_new_tokens=5, use_cache=True, return_dict_in_generate=True) model.tokenizer.decode(generation_output.sequences[0]) To request other model support: here Supported Models AirLLM works out of the box with virtually every popular open LLM — just pass its Hugging Face ID to AutoModel.from_pretrained(...).