Exploring AirLLM: Running Massive 70B LLMs on a 4GB GPU

In the ever-evolving landscape of artificial intelligence, one of the most significant challenges has been the resource demands of large language models (LLMs). Models with billions of parameters, such as the 70B-parameter LLMs, typically require high-end GPUs with substantial VRAM—often 24GB or more—to run efficiently. However, a groundbreaking project called AirLLM is turning heads by enabling these massive models to run on a modest 4GB GPU. In this blog post, we’ll dive into what AirLLM is, how it works, its implications, and how you can get started with it—complete with details, links, and insights based on the latest discussions as of December 31, 2025.

What is AirLLM?

AirLLM is an innovative open-source project designed to optimize the memory usage of large language models during inference. Developed by Gavin Li and hosted on GitHub (https://github.com/lyogavin/airllm), AirLLM allows users to run 70B-parameter LLMs—such as Llama 3.1—on a single 4GB GPU card without relying on traditional model compression techniques like quantization, distillation, or pruning. This is a game-changer for individuals and organizations with limited hardware resources, democratizing access to cutting-edge AI.

The project has garnered significant attention, boasting over 6.5k stars on GitHub, and was recently highlighted in a post by Md Ismail Šojal (@0x0SojalSec) on X (https://x.com/0x0SojalSec/status/2006060751589622043) on December 30, 2025. The post showcased AirLLM’s capability to handle 70B models with layer-wise inference and optional quantization, sparking a lively discussion among AI enthusiasts.

How Does AirLLM Work?

AirLLM achieves this feat through a clever combination of memory optimization techniques and layer-wise offloading. Here’s a breakdown of the key mechanisms:

1. Layer-Wise Offloading

Instead of loading the entire 70B-parameter model (which would require ~130GB of memory in full precision) into the GPU at once, AirLLM splits the model into layers. These layers are offloaded to the CPU and RAM when not in use, with only the necessary layers loaded onto the 4GB GPU for inference. This approach minimizes VRAM usage while maintaining the model’s full precision, avoiding the accuracy trade-offs associated with quantization.

2. Memory Optimization

The project leverages advanced memory management strategies, such as prefetching (introduced in version 2.5), to overlap model loading and computation, improving efficiency by up to 10%. This ensures smooth inference even on low-end hardware.

3. Support for Larger Models

Recent updates have expanded AirLLM’s capabilities. As of version 2.11.0 (August 2024), it supports running the massive 405B-parameter Llama 3.1 model on an 8GB VRAM GPU, further pushing the boundaries of what’s possible with limited resources.

4. Optional Quantization

While AirLLM avoids quantization by default to preserve accuracy, it offers 4-bit and 8-bit block-wise quantization options (introduced in version 2.0) for a potential 3x speedup in inference, with minimal accuracy loss. This is detailed in the project’s documentation and linked to research on quantization (https://arxiv.org/abs/2212.09720), though the core method predates this 2022 study.

Performance and Trade-Offs

While AirLLM is impressive, it comes with trade-offs. Community feedback on X reveals that inference speed is a bottleneck, with estimates ranging from 0.7 tokens per second (as noted by @NOOROU) to as slow as one token per hour in extreme cases. This is largely due to the overhead of layer offloading and disk I/O. For comparison, a user (@Ithilbor) mentioned a trade-off of 30 seconds per word, highlighting the need for patience or faster storage solutions like SSDs.

Despite the speed limitations, the ability to run unquantized 70B models on a 4GB GPU is a remarkable achievement. The project also supports a wide range of models, including Llama 3, Qwen2.5, ChatGLM, and Mistral, making it versatile for various use cases.

Getting Started with AirLLM

Ready to try AirLLM yourself? Here’s a step-by-step guide based on the official GitHub repository:

1. Installation

First, install the AirLLM package via pip:

pip install airllm

For quantization support, also install bitsandbytes:

pip install -U bitsandbytes

2. Inference Example

Initialize a model and run inference using the AutoModel class. Here’s an example for a 70B Llama model:

from airllm import AutoModel
MAX_LENGTH = 128
model = AutoModel.from_pretrained("garage-bAInd/Platypus2-70B-instruct", compression='4bit')  # Optional 4-bit quantization
input_text = ['What is the capital of the United States?']
input_tokens = model.tokenizer(input_text, return_tensors="pt", 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)

This code loads the model layer-wise and generates text, with the option to enable compression for faster performance.

3. MacOS Support

AirLLM also works on MacOS with Apple Silicon, requiring mlx and torch. Check the example notebook: https://github.com/lyogavin/airllm/blob/main/air_llm/examples/run_on_macos.ipynb.

4. Additional Configurations

• Use compression='4bit' or '8bit' for quantized inference.
• Set profiling_mode=True to monitor time consumption.
• Specify layer_shards_saving_path for custom storage of split layers.

Community Insights and Updates

The X thread sparked valuable discussions:

• @MinChonChiSF suggested optimizing for PCIe bottlenecks and context window fragmentation, indicating room for future improvements.
• @inference_eng asked about Mac M1 compatibility, which is supported per the GitHub docs.
• Users like @di_bozh and @MosheRecanati emphasized the need for token speed data, with @NOOROU providing a 0.7 tokens/second estimate.

The project’s changelog highlights continuous development:

• v2.11.0 (Aug 2024): Added Qwen2.5 support.
• v2.10.1 (Aug 2024): Introduced CPU inference and non-sharded model support.
• v2.7 (Dec 2023): Added AirLLMMixtral support.

However, @WanHL7 noted the lack of recent updates, with the last major release in mid-2024, suggesting the project may be stabilizing.

Implications and Future Potential

AirLLM opens doors for edge AI deployment, educational experiments, and small-scale research without the need for expensive hardware. Its ability to handle 405B models on 8GB VRAM hints at future scalability, potentially rivaling commercial solutions. However, addressing inference speed and integrating with frameworks like vLLM (as suggested by Hugging Face resources) could elevate its adoption.

For those interested in contributing, the GitHub repository welcomes ideas and pull requests (https://github.com/lyogavin/airllm).

Conclusion

AirLLM is a testament to the ingenuity of the open-source community, proving that massive LLMs can run on modest hardware with the right optimizations. Whether you’re a hobbyist, student, or developer, this project offers a practical entry point into the world of large-scale AI. Dive into the GitHub repository, experiment with the code, and join the conversation on X to share your experiences.

Links:

• GitHub Repository: https://github.com/lyogavin/airllm
• Original X Post: https://x.com/0x0SojalSec/status/2006060751589622043
• Quantization Research: https://arxiv.org/abs/2212.09720
• MacOS Example: https://github.com/lyogavin/airllm/blob/main/air_llm/examples/run_on_macos.ipynb