faisscpu

faisscpu is the CPU-focused distribution of the FAISS library, developed by Facebook AI Research. FAISS (Facebook AI Similarity Search) provides efficient algorithms for similarity search and clustering of dense vectors, enabling fast nearest neighbor retrieval on large datasets. The faisscpu variant runs entirely on standard CPU hardware and does not require a GPU, making it suitable for environments without CUDA or with CPU-bound workloads. It is open-source under the MIT license and ships with Python and C++ interfaces.

The library implements a broad set of index structures and algorithms. Exact search can be performed with Flat indices, while approximate search uses inverted-file (IVF) indices, product quantization (PQ), and optimized product quantization, as well as other techniques such as OPQ and HNSW-based indexes. Indices can be trained on a sample of vectors when needed, then used to add a database of vectors and perform queries to retrieve nearest neighbors. FAISS supports multiple distance measures, notably L2 and inner product.

Usage patterns generally involve preparing data as dense vectors, choosing an index type, training if required, adding vectors to the index, and performing searches to obtain the indices and distances of the k nearest neighbors. On CPU, FAISS leverages multi-threading and SIMD instructions to maximize throughput. It scales to millions of vectors, especially with IVF-based architectures, and can store compact representations through PQ.

Typical applications include image or text embeddings retrieval, recommender systems, and large-scale anomaly detection. While faisscpu provides strong CPU performance, GPU-accelerated FAISS (faiss-gpu) can substantially accelerate large-scale searches on compatible hardware. Integration with Python via numpy arrays facilitates use in data science workflows. Developers should consider hardware limits, memory usage, and index selection when designing systems.