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How to use Airllm: what you can do with it

What is Airllm? AirLLM cuts inference memory so very large language models run on a single small GPU without quantization, distillation or pruning, by streaming model layers instead of loading the whole model. The README reports 70B models on a 4GB card, DeepSeek-V3 (671B) on about 12GB and Kimi K3 on under 4GB, all through one AutoModel API, plus optional 4-bit or 8-bit compression for speed and new training support for huge models on small VRAM.

How do I use it? Run pip install airllm; then in Python, from airllm import AutoModel and load a model with AutoModel.from_pretrained("Qwen/Qwen3-32B") or another repo ID; Tokenize your prompt with model.tokenizer as in the Quickstart. Full steps below.

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What do you want to do?

3 use cases

Quick start with Airllm

  1. Check you have what it needs: Python and pip; A CUDA GPU (a few GB of VRAM) or a Mac with mlx and torch for Llama-style models; Lots of disk for split layer shards; some models need specific transformers versions.
  2. Open lyogavin/airllm on GitHub and read the README and license.
  3. Run pip install airllm
  4. In Python, from airllm import AutoModel and load a model with AutoModel.from_pretrained("Qwen/Qwen3-32B") or another repo ID
  5. Tokenize your prompt with model.tokenizer as in the Quickstart
  6. Call model.generate with max_new_tokens and decode the output

These steps come from our top use case, Run a 70B-class model on a single 4GB GPU, which also has a copy-paste agent prompt.

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Airllm alternatives · Best AI repos for Software and DevOps

About this repo

  • Who it is forDevelopers and researchers with a modest GPU who want to experiment with very large open models without renting big cloud machines.
  • Why it is hot41k stars, picked up by one AI source we track
  • LanguageJupyter Notebook
  • Sourcegithub.com/lyogavin/airllm · Back to Hot repos

Requirements

  • Python and pip
  • A CUDA GPU (a few GB of VRAM) or a Mac with mlx and torch for Llama-style models
  • Lots of disk for split layer shards; some models need specific transformers versions
  • Optional: bitsandbytes for compression

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Questions about Airllm

What is Airllm?

AirLLM cuts inference memory so very large language models run on a single small GPU without quantization, distillation or pruning, by streaming model layers instead of loading the whole model. The README reports 70B models on a 4GB card, DeepSeek-V3 (671B) on about 12GB and Kimi K3 on under 4GB, all through one AutoModel API, plus optional 4-bit or 8-bit compression for speed and new training support for huge models on small VRAM. It is an open-source project on GitHub: https://github.com/lyogavin/airllm.

How do I use Airllm?

Run pip install airllm; then in Python, from airllm import AutoModel and load a model with AutoModel.from_pretrained("Qwen/Qwen3-32B") or another repo ID; Tokenize your prompt with model.tokenizer as in the Quickstart. The use cases on this page give the full steps and a copy-paste agent prompt for each job.

Who is Airllm for?

Developers and researchers with a modest GPU who want to experiment with very large open models without renting big cloud machines.

What can I do with Airllm?

3 use cases so far, for example: Run a 70B-class model on a single 4GB GPU; Speed up large-model inference with 4-bit block compression; Run large Llama-style models on a Mac with AirLLM. Each has steps and a copy-paste agent prompt.

What do I need to run Airllm?

Python and pip; A CUDA GPU (a few GB of VRAM) or a Mac with mlx and torch for Llama-style models; Lots of disk for split layer shards; some models need specific transformers versions; Optional: bitsandbytes for compression.

What are alternatives to Airllm?

Other hot AI repos in Software and DevOps: Deep Infer, Harness2, Orca, Opencode, Graphify.

Is Airllm safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.