In Depth

GPT-3 has 175 billion parameters; the largest frontier models today exceed one trillion. Parameters store the compressed knowledge acquired during training, but raw parameter count is an imperfect proxy for capability — data quality, architecture, and training procedure matter equally. Researchers also study how parameter count scales with capability in power-law "scaling laws."