GPU Cloud

GPU cloud refers to cloud services that rent graphics processing units by the hour for AI training and inference workloads. Training and serving neural networks depend on massively parallel matrix math, which GPUs perform far faster than general-purpose CPUs. Buying and housing that hardware requires capital, power, and cooling, so most teams rent capacity instead. Providers supply the machines along with fast interconnects, high-throughput storage, and preinstalled drivers and frameworks. The large platforms, AWS, Google Cloud, and Microsoft Azure, all offer GPU instances, while specialized providers such as CoreWeave, Lambda, and Together focus on AI workloads and often price more aggressively. NVIDIA supplies most of the accelerators these providers deploy, which is why its data center business tracks AI demand closely. Access to capacity has itself become a competitive factor, and model developers sign long-term contracts to secure supply. The recurring pitfall is cost. An idle reserved cluster bills the same as a busy one, and inference traffic that runs continuously can cost more over a year than the original training run, so teams watch utilization, use interruptible capacity where downtime is tolerable, and shrink models through quantization or adapter-based tuning.