FreeCodeCamp Guide Lets You Train a 25M-Parameter LLM on a CPU
FreeCodeCamp published a step-by-step tutorial for building and training a 25-million-parameter large language model entirely on a standard CPU, no GPU required. The guide walks through the full pipeline: tokenizer training, transformer architecture implementation, and training loop, using a small dataset to keep compute feasible. A 25M-parameter model is roughly 1/7000th the size of GPT-3, but it's enough to learn core LLM mechanics hands-on. The tutorial targets developers who want to understand how LLMs work under the hood without renting cloud GPUs or buying expensive hardware. It arrives as interest in small, locally-runnable models grows, with tools like llama.cpp and Ollama making on-device inference mainstream.
breakthroughs10/8/2026