Description
Deep-learning workloads can use both MKL CPU optimization and CUDA GPU acceleration through this library build. It is useful for developers and researchers who need flexible compute backends for neural-network work.
AI workloads can process sensitive data and consume heavy CPU, GPU, memory, and power resources. Validate drivers, datasets, and model behavior before deployment.