# NUMA or non-uniform memory access is a memory layout design used in data center machines meant to take advantage of locality of memory in multi-socket machines with multiple memory controllers and blocks. Generally speaking, all deep learning workloads, training or inference, get better performance without accessing hardware resources across NUMA nodes. Thus, inference can be run with multiple instances, each instance runs on one socket, to raise throughput. For training tasks on single node, distributed training is recommended to make each training process run on one socket.
