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Osman's Odyssey: Byte & Build
Chronicles of a Perpetual Learner

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  • Serving AI From The Basement — Part I: 192GB of VRAM Setup

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    A Dedicated AI Server with 8x RTX 3090 GPUs and 192GB of VRAM

    This blogpost was originally posted on my LinkedIn profile in July 2024.

    Backstory: Sometime in March I found myself struggling to keep up with the mere 48GB of VRAM I had been relying on for almost a year in my LLMs experimentations. So, in a geeky-yet-stylish way, I decided to spend my money to build this thing of beauty. Questions swirled: Which CPU/Platform to buy? Does memory speed really matter? And why the more PCIe Lanes we have the better? Why 2^n number of GPUs matter in multi-GPU node setup (Tensor Parallelism, anyone?) How many GPUs, and how can I get all the VRAM in the world? Why are Nvidia cards so expensive and why didn’t I invest in their stock earlier? What inference engine to use (hint: it’s not just llama.cpp and not always the most well-documented option)?

    After so many hours of research, I decided on the following platform:

    And we’re live!

    Now that I kinda have everything in order, I’m working on a series of blog posts that will cover the entire journey, from building this behemoth to avoiding costly pitfalls. Topics will include:

    Stay tuned.

    P.S. I’m sitting here staring at those GPUs, and I just can’t help but think how wild tech progress has been. I remember being so excited to get a 60GB HDD back in 2004. I mean, all the movies and games I could store?! Fast forward 20 years, and now I’ve got more than triple that storage capacity in just one machine’s graphic cards… It makes me think, what will we be doing in another 20 years?!

    Anyway, that’s why I’m doing this project. I wanna help create some of the cool stuff that’ll be around in the future. And who knows, maybe someone will look back on my work and be like “haha, remember when we thought 192GB of VRAM was a lot?”

    Part II of this Blogpost Series is now available here.