What is the impact of thermal throttling in a data center with AI workloads?

Thermal throttling in a data center, particularly with AI workloads, leads to reduced efficiency and slower processing. When the hardware components, such as CPUs or GPUs, reach a certain temperature threshold, thermal throttling is activated to prevent overheating. This mechanism reduces the performance of the components by lowering their clock speeds or disabling some cores to dissipate heat more effectively.

As AI workloads are typically resource-intensive and can generate substantial heat, the likelihood of encountering thermal throttling increases. Consequently, the system's ability to perform computations at optimal speeds is compromised, leading to extended processing times for tasks that could otherwise be executed more quickly. This slowdown not only affects overall performance but also diminishes the data center's ability to efficiently handle large volumes of AI training or inference tasks.

In summary, the impact of thermal throttling significantly slows down processing and reduces the system's overall efficiency, making the provided answer accurate in this context.