Increasing which factor may lead to better throughput but could also increase inference latency?

Increasing the batch size during inference can lead to better throughput, as more data points are processed simultaneously, making better use of computational resources like GPUs. This optimization is particularly efficient because the model can take advantage of parallel processing, reducing the overall time required for handling a larger number of inputs in one go.

However, while throughput improves, the inference latency per individual sample may increase. This is because when a larger batch is processed, the system must wait until all samples in that batch are ready before moving on to the next part of the computation. Thus, if your application requires low latency—for instance, in real-time applications—using a larger batch size might counteract that need, as each individual sample will take longer to process compared to smaller batch sizes.

In contrast, the other factors have different impacts. The learning rate primarily affects the training phase rather than inference throughput or latency. The number of neurons impacts model capacity and complexity but does not directly correlate with throughput or latency in a straightforward way during inference. Finally, increasing training epochs pertains to how long the model is trained, which does not influence inference throughput or latency directly.