CUDA

CUDA (Compute Unified Device Architecture) is NVIDIA proprietary parallel computing platform and programming model that provides developers with direct access to GPU hardware for general-purpose computation. Introduced in 2006, CUDA has become the foundational software layer for the entire AI computing ecosystem, with virtually all major deep learning frameworks, inference engines, and scientific computing libraries built on top of it.

CUDA provides a C/C++ extension that allows developers to write kernel functions executed in parallel across thousands of GPU threads. The programming model abstracts GPU hardware into a hierarchy of threads, blocks, and grids, enabling developers to express parallelism without managing individual cores. Higher-level libraries built on CUDA include cuBLAS for linear algebra, cuDNN for deep neural network primitives, cuFFT for fast Fourier transforms, and NCCL for multi-GPU communication.

The CUDA ecosystem extends far beyond the core programming model. PyTorch and TensorFlow, the dominant deep learning frameworks, rely heavily on CUDA and cuDNN for GPU-accelerated tensor operations. TensorRT provides CUDA-based inference optimization with kernel fusion, quantization, and layer optimization. Triton Inference Server uses CUDA for high-performance model serving. The entire NVIDIA AI software stack from NeMo to NIM is built on CUDA foundations.

CUDA competitive moat is a significant factor in NVIDIA market dominance. The depth of the CUDA ecosystem, including libraries, tools, documentation, and developer expertise accumulated over nearly two decades, creates high switching costs that make it difficult for competing GPU architectures to gain traction in AI workloads despite potential hardware advantages. Understanding CUDA capabilities and limitations is important for AI infrastructure planning, as it influences hardware selection, software compatibility, and optimization strategies.