Running Inference on a 405B Model with an RTX 4070 Super – Thoughts on Optimizing Further?

AI & Data Science AI Foundation Models and Endpoints
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Hey everyone,

I’m new here, but I’ve been diving deep into AI model optimization lately and wanted to share something I’ve been working on. I managed to get inference running on the 405B LLaMA model with just a 4070 Super GPU. It took me about 26 hours to complete, but it worked! I’m pretty excited about this since I’m using consumer-grade hardware, and I’m curious if anyone else has tried something similar.

While working on this, I’ve been playing around with an idea I’m calling a VPool System—basically a framework to dynamically allocate GPU resources to handle larger models without needing high-end hardware. It’s still a work in progress, but here’s what I’ve tried so far:

  • Dynamic VRAM Pooling: Preloading model layers based on predictions to stay within the GPU’s VRAM limits.
  • Async Offloading: Offloading inactive layers to CPU or even NVMe storage when GPU memory gets tight.
  • Quantization: Dropping the precision down to 4-bit to save memory while keeping performance reasonable.

I’ve got two main questions for you all:

  1. Has anyone else run inference on models this size with similar (or less) hardware? If so, I’d love to hear how you approached it.
  2. For those who’ve experimented with asynchronous offloading, what’s the best way to speed it up or avoid bottlenecks?

This project has been a mix of wins and trial-and-error for me, so I’d love any insights or advice from those who’ve tackled these kinds of challenges.

Looking forward to hearing from you and learning more about what’s possible in this space!

Cheers,
Ross

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We’re gonna need a github repo or it didn’t happen.

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I’ve developed an optimized inference pipeline that leverages offloading and mixed precision to efficiently run large language models on consumer GPUs (like the 4070 Super with 12GB VRAM). The project—named Vpool—demonstrates how to keep GPU memory usage low while still achieving effective inference.

The repository includes an optimized Python script along with documentation on installation and usage.

GitHub Repository:

https://github.com/ftrou/Vpool

Installation and Usage Instructions:

How to Use:

Clone the repository:

git clone https://github.com/ftrou/Vpool.git

Navigate into the project folder:

cd Vpool

Install the required dependencies:

pip install -r requirements.txt

Run the inference script:

python 405vpool.py

The script will load the model, run inference on a sample input, and print performance metrics.

License & Patent Notice:
This project is covered under a proprietary license, and aspects of the technology are patented. Please refer to the LICENSE file in the repository for details.

I welcome feedback, questions, or suggestions for improvement. Feel free to reach out via GitHub or reply to this thread.