CUDA

Overview

CUDA, short for Compute Unified Device Architecture, is the software layer NVIDIA created to open its GPUs up to general-purpose programming. Before it, a GPU's parallel power was locked behind graphics-specific interfaces. CUDA lets developers write ordinary C, C++, Fortran, or Python that executes directly across thousands of GPU cores, turning a graphics chip into a general parallel processor.

How it works

A CUDA program marks the parallelizable parts of its work and launches them as kernels, functions that run simultaneously across thousands of lightweight GPU threads. The platform handles distributing those threads across the hardware and moving data between CPU and GPU memory. Crucially, the major deep-learning frameworks are themselves built on CUDA, so most developers benefit from it without writing a single kernel.

Why it matters

CUDA, more than any one chip, is why NVIDIA came to dominate AI. A decade and more of libraries, tools, and accumulated developer knowledge built atop it created an ecosystem competitors have found very hard to dislodge. For users it means portability: code that runs on one NVIDIA GPU runs on any other, which is exactly why workloads land on Eleveight AI's cluster untouched.