The runtime loads parameters, converts inputs into tensors, performs forward computation, and decodes or postprocesses outputs. Serving choices determine speed, cost, precision, and concurrency without ordinarily changing the learned weights.
Model inference is the execution of a trained model on new inputs to produce scores, predictions, embeddings, or generated content.
The runtime loads parameters, converts inputs into tensors, performs forward computation, and decodes or postprocesses outputs. Serving choices determine speed, cost, precision, and concurrency without ordinarily changing the learned weights.