I built a tiny LPU to run MicroGPT from scratch

Most university programs completely fail when it comes to teaching actual chip design. My peers and I sat through digital logic courses where the "pinnacle" of the semester was just building a basic full adder in Quartus using logic blocks. We never even touched RTL (Register-Transfer Level). It felt incredibly disconnected from how the real world works, so we decided to stop waiting for a curriculum and just build something meaningful on our own. We wanted to see if we could strip away the mystery of AI hardware and prove that modern accelerators aren't magic—they are just clever applications of basic math like $y = mx + b$ and fundamental logic circuits.

Our specific mission was to design a "lite" version of a Language Processing Unit (LPU) and use it to run inference on Andrej Karpathy’s MicroGPT. We weren't trying to build a commercial competitor to Groq, but we wanted to answer a very specific question: What exactly makes the LPU architecture so much more compelling for LLMs that even companies like NVIDIA would look at it?

The architecture philosophy

Instead of following a standard GPU roadmap, we focused on the core principles that make an LPU effective for transformer-based models. While a GPU relies on massive parallelism and complex memory hierarchies to hide latency, an LPU-style architecture aims for deterministic execution and high-speed data movement.

We approached this as an educational deep dive rather than a formal "how-to" guide for silicon manufacturing. The goal was to map out the journey of how a person with minimal hardware engineering experience can actually start approaching the field of AI acceleration.

Breaking down the implementation

To get this working, we had to bridge the gap between high-level machine learning concepts and low-level hardware description. Here is how we structured our thinking:

• Mathematical Foundation: We realized that at its heart, the transformer architecture is a massive sequence of matrix multiplications and non-linear activations. If you can build a hardware path that optimizes these specific operations, you've won half the battle.
• Data Flow over Control Logic: Unlike a general-purpose CPU that spends a lot of energy on branch prediction and complex instruction decoding, our design focused on keeping the data moving through the compute units.
• The MicroGPT Testbed: Using Karpathy's MicroGPT was the perfect "litmus test." It’s small enough to be computationally manageable but complex enough to require a proper understanding of the transformer block structure.

Why this matters for AI workflows

If you are looking into prompt engineering or LLM agent development, understanding the hardware layer is actually quite useful. It helps you understand why certain model architectures are more "expensive" to run in terms of latency versus throughput. When we were designing our lite LPU, we saw firsthand how much the memory bottleneck dictates the way we have to structure our AI workflows.

This project wasn't a 1:1 replica of a professional LPU, but it served as a proof of concept. It proved that you don't need a PhD in VLSI to start experimenting with how AI models actually interact with silicon. If you can understand the math of a linear layer, you can start to visualize the hardware required to accelerate it.