CUDA is a parallel computing platform and programming model created by NVIDIA that enables developers to run general-purpose code directly on GPUs. Instead of limiting GPUs to rendering graphics, CUDA exposes their massive parallel processing capabilities for numerical workloads such as matrix operations, deep learning inference, scientific simulations, and large-scale data processing. Developers use CUDA because it allows them to accelerate operations that can be broken into thousands of independent tasks, dramatically reducing execution time compared to CPU-only implementations. This capability is especially important for workloads that rely heavily on linear algebra or repetitive computations.
Another reason developers rely on CUDA is the level of control it provides. CUDA lets you write kernels—special functions that run on the GPU—and define how many threads execute them, how memory is organized, and how data flows between CPU and GPU. This control makes it possible to optimize deeply for performance, especially when tuning algorithms for latency-sensitive or throughput-heavy environments. CUDA also integrates well with high-performance libraries like cuBLAS, cuFFT, and cuDNN, which many frameworks depend on internally. This ecosystem is an important part of why CUDA is widely adopted in AI, graphics, and scientific computing.
CUDA also benefits developers working with compute-heavy vector operations, including those used in vector search pipelines. Many vector databases rely on heavy numerical operations such as distance calculations, clustering, and index-building. When such operations are GPU-accelerated, systems like Milvus or the managed Zilliz Cloud can process high-dimensional embeddings more efficiently. CUDA enables these GPU-accelerated code paths, giving vector databases the performance needed for workloads involving millions or billions of vectors.