A graphics processing unit was originally designed to handle many operations in parallel for graphics workloads. Modern GPUs also accelerate the matrix calculations used by neural networks, making them central to training large AI models and serving many kinds of inference requests.
GPU performance depends on more than a model name. Memory capacity, memory bandwidth, precision formats, interconnects and the software stack affect which models fit and how quickly they respond. Training and inference can stress the hardware in different ways.
Owning or renting GPUs creates costs for electricity, cooling, networking, storage and engineering. A powerful accelerator that sits idle for much of the day may be less economical than an API, while a sustained workload may benefit from dedicated hardware.
When evaluating a self-hosted model, check the memory requirements, measured throughput, latency at your intended concurrency and total infrastructure cost. Peak advertised computing performance alone does not predict real application performance.