Build a Multi-GPU RAG System: A Self-Hoster's Guide

8/11/2025

The Self-Hoster's Guide: Building a Multi-GPU RAG System with Local MCP Integration

Hey everyone. So, you've been playing around with AI models, probably hitting some APIs, & you've started to feel... constrained. The rate limits are a pain, you're a little fuzzy on where your data is really going, & you just can't shake the feeling that you could be doing SO much more if you just had a bit more control. If that sounds familiar, then you've come to the right place.

We're about to dive deep into the world of self-hosting your own AI powerhouse. I'm not just talking about running a dinky model on your gaming rig. I'm talking about building a full-blown, multi-GPU Retrieval-Augmented Generation (RAG) system, complete with local Model Context Protocol (MCP) integration. It sounds like a mouthful, but honestly, it's the future of building truly custom, powerful, & private AI solutions.

This is for the builders, the tinkerers, the folks who want to pop the hood & get their hands dirty. It's a journey, for sure, but the payoff is having an AI system that's all yours, tailored to your exact needs, & free from the limitations of the big cloud providers.

The Anatomy of a Self-Hosted RAG System

First things first, let's break down what a RAG system actually is. You've probably heard the term thrown around, but it's pretty simple at its core. Instead of relying solely on the stuff a large language model (LLM) learned during its initial training, RAG lets you "augment" its knowledge with your own data, in real-time. This is HUGE, because it means the model can answer questions about very specific, up-to-the-minute information.

Here's a quick rundown of the moving parts:

Data Ingestion & Preprocessing: This is where it all starts. You gather up all the data you want your AI to know about – PDFs, documents, website content, your company's entire Slack history, whatever. Then you chop it up into manageable "chunks." Think of it like tearing pages out of a textbook to make them easier to study.
Embedding Generation: This is the magic step. You take those chunks of text & use a special kind of model (an embedding model) to turn them into a string of numbers, called a "vector." This vector represents the meaning of the text. It's how the AI can understand semantic relationships between different pieces of information.
Vector Storage: All those vectors need a place to live, & that place is a vector database. Think of it as a super-smart library for your AI's memories. When you ask a question, the AI can quickly search this database to find the most relevant "memories" (i.e., the most similar vectors). Qdrant is a popular choice here, & you can run it in a Docker container, which is pretty convenient.
Retrieval: This is where the "retrieval" in RAG comes in. When you ask your AI a question, it first turns your question into a vector, then it goes to the vector database & says, "Hey, find me all the chunks of text that are most similar to this question." The database then returns a list of the most relevant chunks.
Generation: Now for the "generation" part. The AI takes your original question, the relevant chunks of text it just retrieved, & feeds them all into a powerful LLM. The LLM then uses this information to generate a coherent, context-aware answer. It's not just regurgitating what it already knew; it's synthesizing new information to give you a custom-tailored response.

This whole pipeline is what makes RAG so powerful. You're not just asking a question to a generic AI; you're having a conversation with an AI that has read all of your documents & can use them to inform its answers.

Going Multi-GPU: Unleashing the Beast

Okay, so you've got the RAG concept down. Now, let's talk about the hardware. If you're serious about this, you're going to want more than one GPU. Why? Because the models you'll be using are BIG. We're talking billions of parameters. Running them on a single GPU is possible, but it can be slow, especially if you want to use the really good models.

A multi-GPU setup gives you a few key advantages:

Speed: More GPUs means you can process things faster. This is crucial for real-time applications like chatbots, where you can't have the user waiting around for an answer.
Scale: With multiple GPUs, you can run much larger, more powerful models. This means more accurate answers & a more capable AI.
Reliability: If one GPU has an issue, the others can pick up the slack. It's a level of redundancy that's hard to get with a single card.

So, what kind of hardware are we talking about? A great real-world example comes from a user on Reddit who was building a similar system. They were using an AMD Threadripper CPU (great for lots of PCIe lanes), a motherboard that could handle multiple GPUs at full bandwidth, & a couple of NVIDIA RTX 3090s, each with 24GB of VRAM. This is a solid setup that can handle some serious AI workloads.

But the hardware is only half the battle. You also need the right software to make all those GPUs work together. This is where things get interesting. A lot of people start with a tool called

llama.cpp

. It's fantastic for running models on a single GPU, or even for offloading some layers to the CPU if you're short on VRAM. But, and this is a big "but," it's not the best choice for a true multi-GPU setup.

For that, you're going to want to look at something like

vLLM

is designed from the ground up for distributed inference, which is a fancy way of saying it's really, really good at splitting up a model across multiple GPUs & making them work together efficiently. It uses a technique called "tensor parallelism" to get a massive performance boost.

Setting up

vLLM

is surprisingly straightforward. You can use it as a server that you can then send requests to. A typical command might look something like this: