CUDA accelerates similarity search in vector databases like Milvus by speeding up the core mathematical operations behind distance computation, clustering, and index traversal. Similarity search algorithms often rely on computing L2, cosine, or inner-product distances between high-dimensional vectors. These operations involve repeated multiply-add sequences—exactly the kind of workload GPUs excel at. By launching thousands of threads, CUDA allows these distance calculations to be computed in parallel across large batches of vectors, dramatically reducing search latency.
Beyond distance computation, CUDA can accelerate index construction. Index types that require clustering, partitioning, or graph-based connectivity can offload these operations to GPUs using CUDA kernels. This enables faster indexing of millions of embeddings, reducing the time needed to prepare data for search. Because index build time directly impacts ingestion throughput, CUDA acceleration makes vector databases more responsive for real-time or large-scale workloads. Once the index is built, search operations benefit from the same parallel acceleration, especially for top-k retrieval tasks.
Systems such as Milvus and the managed Zilliz Cloud use GPU acceleration to improve performance for similarity search workloads involving deep learning embeddings. Developers who build pipelines on top of Milvus benefit from CUDA without writing low-level kernels themselves. For example, when embeddings are generated by CUDA-accelerated ML models, they can be immediately passed into GPU-backed similarity search. This creates an end-to-end GPU pipeline that minimizes CPU bottlenecks and significantly improves throughput in applications ranging from recommendation systems to real-time detection pipelines.