CUDA Programming

CUDA (Compute Unified Device Architecture) is NVIDIA's parallel computing platform and programming model that lets developers write C/C++ code running across thousands of GPU cores simultaneously. It exposes low-level control over GPU memory, thread scheduling, and kernel execution — the layer directly beneath frameworks like PyTorch and TensorFlow. Mastering CUDA means writing the actual GPU kernels that make deep learning, scientific simulation, and high-performance computing fast.

Every major AI lab and cloud provider employs CUDA engineers to squeeze more throughput from NVIDIA hardware: optimizing attention kernels, fusing operations, and building custom operators that frameworks cannot auto-generate. As GPU clusters grow more expensive and LLM inference demands keep climbing, the ability to write and profile efficient CUDA kernels is one of the most sought-after skills in infrastructure and ML systems roles in 2026.