What is the role of CUDA in enabling GPU-accelerated offline AI?
CUDA is NVIDIA parallel computing platform that enables general-purpose computing on NVIDIA GPUs. For AI, CUDA provides the foundational layer that all major deep learning frameworks build upon. cuDNN optimizes neural network primitives like convolutions and attention. cuBLAS accelerates the dense matrix multiplications central to transformer inference. CUDA supports multiple precision modes including FP32, FP16, BF16, INT8, and FP8 on newer architectures. The CUDA toolkit includes profiling tools for identifying bottlenecks, memory management utilities, and multi-GPU communication libraries. For offline AI, CUDA compatibility is the primary reason NVIDIA GPUs dominate AI inference. Understanding CUDA memory management, stream scheduling, and kernel launch overhead helps optimize offline AI serving performance. CUDA graphs can further reduce kernel launch overhead for repetitive inference workloads.