AI

For about 3 weeks now, I have been working on a multi-agent system that simulates a team of Software Engineers; this system assigns projects, creates teams and adds members to them based on areas of expertise and need, and asks team members to build features, assign story points, have pair programming sessions together, etc. Started mainly for fun and exploration, however, last week the following paper was released: Agents in Software Engineering .

The paper delivers an overview of a framework that allows large language models play nicely within a sandbox for Software Engineering, and it cites several dozens of papers that implement task-specific agents. Since then, I have been a lot more motivated to get this agentic framework semi-decently put together, and it got me wondering: maybe it will beat Replit?

Overview of SWE Agentic Framework

The time is 02:43 AM as I am writing this paragraph, Mr. Robot OST is playing in the background (all 8 volumes on loop, shuffled of course I am not an animal), and I just spent about ~5 hours on what I assumed would be a quick 2-3 mins task. In that time, I read about half a dozen quantization algorithms, another half dozen model architectures, and dove into GitHub exploring inference engines, libraries, and a lot of LLMOps tools that I was not aware of. I cannot sleep because I like when things work and I DO NOT like when things do not work. Stubbornness is essential when working in Software.

The vLLM inference engine, which I primarily use and is also widely utilized as a kernel in other engine implementations including SGlang , Aphrodite , and TensorRT-LLM , supposedly allows for Mixed Precision quantizations. However, the reality is more complex…

Well, as I said, it is complicated… My AI Server has 192GB of VRAM, and sometimes I would move my main RTX 4090 & RTX 3090 from my PC to the AI Server and that increases the VRAM to 240GB, and I am not typically a fan of that and neither is Tensor Parallelism. Llama 3.1 70B BF16 (Full Precision) has been my main driver model since release, and sometimes I switch to Llama 3.1 405B INT4 (Mixed Precision: 4-bits weights and 16-bits activation, aka W4A16).

Resident AI Server In The Basement

For my SWE multi-agent system, I decided to switch the to DeepSeek v2.5 . DeepSeek v2.5 is a State of The Art (SOTA) coding model, it is a Mixture of Experts (MoE) model with 128k context length, made of 236B parameters. and according to benchmarks the only model that beats it is Claude 3.5 Sonnet. But first, let’s understand a few things.

Imagine Large Language Models as complex Lego sets with Transformers as the basic bricks. However, each set has different instructions—some build from left to right, others can see the whole picture at once, and some might even shuffle the pieces around for a unique approach. Plus, each set might have its own special pieces or ways of snapping the Legos together, making each model unique in how it constructs or understands language.

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.