An inference system is a specialized software and hardware computing framework designed to deploy and execute trained machine learning models to generate predictions or outputs from new data. Unlike model training platforms that focus on updating parameters and calculating gradients, inference systems are engineered for the operational phase of model serving, prioritizing metrics such as low latency, high throughput, and cost-efficient resource utilization. These systems orchestrate execution across computational resources like GPUs, CPUs, and storage devices, coordinating memory management and data flow to handle varying workload demands. To maximize performance under hardware constraints, inference systems often incorporate runtime optimization techniques, including dynamic request batching, model weight and cache compression, precision quantization, tensor scheduling, and distributed offloading strategies.
