BATCH PROCESSING OF REQUESTS FOR TRAINED MACHINE LEARNING MODEL

Memory management is provided for processors, such as GPUs used to process data using a trained machine learning model. Requests received to a CPU can be stored to a request queue until the queue is full, or until a timeout value has been reached for periods of lower activity. The requests can then be batched and sent to a GPU as a single message on a single thread. Memory can be pre-allocated, and the trained model loaded into GPU memory once for processing of the relevant batches. The individual requests can be processed by the GPU and the results analyzed to determine at least a subset of results to return to the CPU, which can be provided back as results of the processing.