What is parallelism in inference?

Parallelism in inference is the simultaneous execution of multiple operations or processing of multiple model requests to accelerate output results.

For example, if you have a neural network that needs to process a large number of input data, parallelism allows:

• Processing multiple requests simultaneously (batch inference).
• Distributing computations within the model itself using multiple CPU or GPU cores.

This increases system throughput and reduces latency.

Example: using TensorFlow or PyTorch, you can feed data batches so that the model predicts for several examples at once, which is more efficient than doing it one by one.

# Batch inference in PyTorch
outputs = model(batch_inputs)  # batch_inputs is a tensor with multiple examples

You can also parallelize inference across multiple threads or processes if the model and infrastructure support it.