GPUs dominate AI infrastructure headlines, but TPUs are a genuinely different kind of chip built for a narrower purpose.
A GPU packs thousands of relatively general-purpose cores designed to run many different kinds of parallel workloads well, originally graphics rendering, now also AI training and inference. That generality is a real strength: the same chip family handles a huge range of tasks reasonably well.
A TPU, Tensor Processing Unit, is purpose-built specifically for the matrix multiplication operations that dominate neural network math, and little else. That narrower focus lets it be more efficient per watt and per dollar for AI workloads, at the cost of being far less useful for anything outside that lane.
Companies running massive, predictable AI workloads at scale, Google’s own infrastructure being the clearest example, benefit from a purpose-built chip’s efficiency. Companies needing flexibility across varied workloads generally stick with GPUs, more widely available across most cloud providers.
Unless you’re renting raw compute directly, most people access AI capability through an API where the underlying chip choice has already been made. What matters practically is the resulting price and performance, covered in more depth in our explainer on how AI chips are made. See NVIDIA’s own Vera Rubin page for the current GPU generation.
