MicroGPT Emerges as Blueprint for Ultra-Compact Transformer Models

A detailed technical blueprint for a stripped-down transformer model called MicroGPT has surfaced, offering a deliberately compact alternative to large language models. Unlike commercial LLMs with billions of parameters, MicroGPT's architecture operates at a fraction of the scale—using just 16-dimensional embeddings and 4 attention heads—while retaining core transformer mechanics. This design makes it viable for deployment on edge devices, microcontrollers, or educational environments where computational resources are limited.

At its core, MicroGPT follows a standard decoder-only transformer flow but with intentional simplifications. Input tokens and positional encodings merge into a 16-dimensional vector (denoted as x = Wtok[i] + Wpos[p]), followed by RMS normalization. The model then processes data through a single transformer block: it computes query, key, and value vectors (q, k, v), updates a rolling cache for autoregressive generation (ki = cacheK + k, vi = cacheV + v), and executes multi-head attention across four parallel heads. Each head operates on a 4-dimensional slice of the query vector, calculates attention scores via dot products and softmax, then outputs weighted value vectors. These are concatenated, projected via a weight matrix (WO), and merged with residual connections.

Following attention, a multilayer perceptron (MLP) block applies a ReLU-activated linear layer to expand dimensions to 64 before collapsing back to 16, again using residual connections. Finally, a language modeling head (Wlm) projects outputs to vocabulary space for token sampling.

This architecture prioritizes transparency and efficiency over performance. By capping dimensions and using a single transformer layer, MicroGPT sidesteps the computational demands of models like GPT-3 while demonstrating transformer fundamentals. Its design allows real-time execution on devices as simple as Raspberry Pi or embedded systems, opening doors for on-device AI applications without cloud dependencies. Trade-offs include limited contextual understanding and lower accuracy compared to larger models, but its value lies in educational accessibility—students and researchers can experiment with transformer internals without expensive hardware.

Such micro-scale models represent a growing counter-trend to the 'bigger is better' ethos in AI. Projects like MicroGPT could accelerate innovation in federated learning, privacy-preserving AI, and STEM education by demystifying complex architectures. While not a commercial product yet, its open specification invites community iteration, potentially inspiring new approaches to efficient neural design.