Running a 70B Ultra-Large AI on a 4GB Graphics Card? Is It For Real?

한국어 English 日本語 简体中文 繁體中文
By Gi Pyeong Lee
AI AirLLM LLM Deep Learning Artificial Intelligence
Large AI model running on a standard home PC
AI Summary

AirLLM allows you to run a 70B model in a 4GB VRAM environment without expensive equipment by loading AI model layers from the disk one by one.

Imagine this: You are excited to try out the latest artificial intelligence (AI) model you have been interested in, but you click the executable file only to be frustrated by a warning that it cannot run on your computer’s specifications.

It has long been considered essential to have expensive, professional-grade equipment like the A100—costing tens of thousands of dollars—to run high-performance AI models, such as the 70B (70 billion parameters, a figure representing the AI’s “brain cells”) models we frequently encounter [Source 11]. However, a recently emerged technology called ‘AirLLM’ is completely shattering this stereotype. Now, you can run massive AI models with just a single 4GB VRAM (video RAM, memory dedicated to the graphics card) card in a standard home PC [Source 1, Source 9].

Why Is This Important?

While AI technology is advancing day by day, the hardware requirements have been a massive barrier to entry for individual users. Until now, experiencing smarter AI meant having to purchase a more expensive computer.

AirLLM solves this cost issue. It is being praised for accelerating the true ‘democratization of AI’ by opening an era where anyone can experiment with and research large language models (LLMs) on their own PC without expensive equipment [Source 13, Source 14].

AD

Working Principle: The Desk and Encyclopedia Analogy

Let me explain the core idea of AirLLM simply. Running an AI model usually is like spreading a thick encyclopedia (a 70B model) consisting of thousands of pages onto a desk (graphics card memory) to read it. Naturally, if the desk is too small, you cannot spread the book out, making it impossible to run.

On the other hand, instead of spreading the whole book out, AirLLM chooses to pull only one necessary page (model layer) from the disk, read it, process the content, and then clear it away before moving to the next [Source 5, Source 9]. This allows you to process the vast information of the entire encyclopedia with just a tiny desk.

What is even more amazing is that it does not use methods that summarize or delete book content (such as quantization, distillation, or pruning). It drastically reduces the memory burden without damaging the model’s performance, allowing it to exert its inherent intelligence fully [Source 1, Source 8].

How Far Has It Come?

Currently, AirLLM is open-source and freely available for anyone to use [Source 1]. Beyond just 70B models, the 405B parameter Llama 3.1 model can be run in an 8GB VRAM environment, and it is even possible to run massive models with a 671B scale on consumer hardware [Source 5, Source 9].

Of course, because it is a method of sequentially loading layers from the disk, it may be slower than methods that load the entire model into memory. However, the fact that you can overcome hardware limitations and run the model itself is a massive technological leap.

Future Outlook

In the future, the need to give up on AI research while blaming your computer specifications will gradually disappear. Efficient optimization technologies like AirLLM will continue to evolve, providing an environment where individual developers and researchers can build their own specialized AI models much more easily. We are entering an era where the ‘size’ of the technology matters less than the ‘size of your ideas’.

References

  1. GitHub - lyogavin/airllm: AirLLM 70B inference with single 4GB GPU · GitHub
  2. Unbelievable! Run 70B LLM Inference on a Single 4GB GPU with This NEW Technique
  3. GitHub - BoxOfllc/AIRllm: AirLLM 70B inference with single 4GB GPU · GitHub
  4. [AirLLM and “70B on a 4GB GPU” — What’s Actually Going On? by Rohit Shirke Medium](https://rohit-shirke.medium.com/airllm-and-70b-on-a-4gb-gpu-whats-actually-going-on-3bf0e102252e)
  5. [AirLLM: Run 70B LLM on 4GB GPU, No Quantization (2026) explainx.ai Blog explainx.ai](https://explainx.ai/blog/airllm-run-70b-llm-4gb-gpu-inference-2026)
  6. GitHub - lyogavin/airllm: AirLLM 70B inference with single 4GB GPU
  7. GitHub - jaganthoutam/airllm-ui: AirLLM 70B inference with single 4GB GPU
  8. 70B 모델을 4GB GPU로 추론하는 오픈소스 ‘AirLLM’ 깃허브서 주목
  9. The Complete AirLLM Guide: Run 70B LLMs on a 4GB GPU
  10. bytewizard42i/airllm-johns-copy: AirLLM 70B inference with single…
  11. GitHub - lyogavin/airllm: AirLLM 70B inference with single 4GB GPU
  12. [Теперь можно запускать 70B LLM на видеокарте с 4GB VRAM Дзен](https://dzen.ru/a/aYMHWtdpuBBf_YnZ)
  13. airllm-AirLLM 70B inference with single 4GB GPU · GraphCanon
  14. GitHub - lyogavin/airllm: AirLLM 70B inference with single 4GB GPU
  15. Airllm AI Project Repository Download and Installation Guide
  16. [AirLLM: 70B Parameter Inference on 4GB GPUs via… AISignal](https://www.aisignal.dev/analysis/lyogavin-airllm)
  17. GitHub - lyogavin/airllm: AirLLM 70B inference with single 4GB GPU
AD
Test Your Understanding
Q1. What is the core principle that allows AirLLM to run 70B models on small memory?
  • Quantization to reduce model size
  • Loading model layers from disk one at a time
  • Using cloud servers
AirLLM solves the out-of-memory issue by processing the model layer-by-layer rather than loading the entire model into memory.
Q2. Which technique does AirLLM use to maintain model performance?
  • Quantization
  • Distillation
  • N/A (Pure inference optimization)
AirLLM optimizes inference while maintaining performance without using techniques like quantization, distillation, or pruning.
Q3. What is the maximum scale of models that can be run with AirLLM?
  • 70B
  • 405B
  • 671B or larger
Even models with up to 671B parameters can be run on consumer hardware.
    Running a 70B Ultra-Large A...
    0:00