System optimizations have come a long way and can substantially reduce latency and cost for DL model inference, but they are not immediately accessible.The complex nature of the DL model inference landscape, including wide variations in model size, architecture, system performance characteristics, hardware requirements, etc., is the primary cause of this lack of availability.It enables low-cost deployment of these models on-premises and on Azure via AML with only a few lines of code, all while providing state-of-the-art performance.MII automatically applies DeepSpeed-system Inference’s optimizations to minimize latency and maximize throughput based on the model type, size, batch size, and available hardware resources.MII can reduce latency by up to 6x for various open-source models across various workloads, making it ideal for latency-critical cases where a batch size of 1 is commonly employed."