Triton supports batch inferencing by allowing individual inference requests to specify a batch of inputs. The inferencing for a batch of inputs is performed at the same time which is especially important for GPUs since it can greatly increase inferencing throughput. In many use cases the individual inference requests are not batched, therefore, they do not benefit from the throughput benefits of batching.
The inference server contains multiple scheduling and batching algorithms that support many different model types and use-cases. More information about model types and schedulers can be found in Models And Schedulers.
The default scheduler is used for a model if none of the scheduling_choice properties are specified in the model configuration. The default scheduler simply distributes inference requests to all model instances configured for the model.
The ensemble scheduler must be used for ensemble models and cannot be used for any other type of model.
The ensemble scheduler is enabled and configured independently for each model using the ModelEnsembleScheduling property in the model configuration. The settings describe the models that are included in the ensemble and the flow of tensor values between the models. See Ensemble Models for more information and examples.