What is the likely cause for varying inference times across instances using the same configuration on NVIDIA GPUs?

Varying inference times across instances with the same configuration on NVIDIA GPUs can largely be attributed to variability in the GPU load due to other tenants on the same physical hardware. In multi-tenant environments, such as cloud-based GPU services, multiple users may share the same physical GPU resources. The competition for GPU time and processing power can lead to fluctuations in performance, resulting in inconsistent inference times.

When multiple workloads are leveraging the GPU simultaneously, the available resources (like memory bandwidth and computational power) can be dynamically allocated among different users and their applications. This shared usage creates contention, which can manifest in slower inference times for some instances, particularly if they have to wait for access to the GPU or if other demanding tasks are running concurrently.

The other options highlight issues that, while they could affect performance in different contexts, are less likely to cause variability in inference times in this specific scenario. For instance, differences in CUDA toolkit versions would generally lead to completely different performance characteristics across instances rather than just variability. Similarly, if the model architecture were unsuitable for GPU acceleration, one would expect consistently poor performance rather than variable results. Lastly, network latency between cloud regions would affect data transfer times rather than the inference times directly linked to GPU processing capabilities.