What could cause performance degradation in inference workloads on an NVIDIA GPU cluster?

Insufficient GPU memory allocation can significantly impact the performance of inference workloads on an NVIDIA GPU cluster. Inference processes often require a substantial amount of memory to store model parameters, intermediate activations, and other data needed for computations. When the allocated memory is insufficient, the system may resort to using slower memory types, such as system RAM, or may need to swap data in and out of GPU memory frequently. This introduces latency and overhead that can severely degrade performance, leading to increased inference times and reduced throughput.

Moreover, running out of GPU memory can also trigger errors or cause the system to throttle performance to accommodate lower memory availability, further exacerbating performance issues during inference tasks. Therefore, ensuring that there is sufficient GPU memory allocated is crucial for maintaining optimal performance in inference workloads.