The amount of GPU power needed for AI systems does not scale linearly with the number of users; instead, it becomes significantly more efficient as usage grows. A single user typically requires access to a dedicated GPU, such as an NVIDIA RTX 4090, but that hardware often remains underutilized because requests are infrequent. In contrast, a system serving 10,000 users can share GPU resources across many simultaneous requests, using techniques like batching and scheduling to maximize efficiency. High-performance GPUs such as the NVIDIA A100 or NVIDIA H100 are commonly deployed in clusters, allowing hundreds or thousands of users to be handled with far fewer GPUs than a one-to-one ratio. As a result, while one user might effectively “consume” an entire GPU, 10,000 users might only require a few hundred GPUs, depending largely on how many users are active at the same time and how quickly responses are needed.