GPU-Accelerated Indexing in LanceDB

Vector databases are crucial for applications such as RAG, RecSys, and computer vision, but building vector indices can be computationally intensive, especially as the number of vectors or their dimensions increases. Recent advancements have focused on reducing this bottleneck by incorporating GPU acceleration with tools like LanceDB, which now supports using Nvidia GPUs and Apple Silicon for index training. This enhancement leverages PyTorch for training IVF clusters and benefits from CUDA and MPS support, significantly speeding up processes like KMeans training, as demonstrated by benchmark tests showing up to 26x performance improvements over CPUs. Further enhancements, including GPU support for PQ training and vector assignment, are underway, promising even greater reductions in index training times. LanceDB’s approach facilitates large-scale distributed GPU training and offers potential future integration with other hardware accelerators, paving the way for rapid index training on extensive datasets and potential uses in inference.