Why GPUs Power Large Language Models: The Journey From Keypress to AI Response

When a user submits a query to an AI assistant like ChatGPT or Gemini, the request travels from their device to cloud servers where a series of computational steps begins. The text is first broken into numerical tokens, then converted into high-dimensional vectors called embeddings that represent word meanings. These embeddings are loaded into GPU memory, where the model's transformer layers perform billions of matrix multiplication operations to generate a response. GPUs are suited for this workload because they contain thousands of cores capable of executing such calculations simultaneously, unlike CPUs which process tasks sequentially. Originally designed for rendering video game graphics, GPUs were found to be ideal for the parallel mathematical operations that underpin modern large language models.