Product Quantization: Compressing high-dimensional vectors by 97% | Pinecone
Pinecone lets you add vector search to applications without knowing anything about algorithm optimizations, and it’s free to try. However, we know you like seeing how things work, so enjoy learning about memory-efficient search with product quantization!
Product Quantization: Compressing high-dimensional vectors by 97% Jump to section What is Quantization How Product Quantization Works PQ Implementation in Faiss References Pinecone lets you add vector search to applications without knowing anything about algorithm optimizations, and it’s free to try . However, we know you like seeing how things work, so enjoy learning about memory-efficient search with product quantization! Vector similarity search can require huge amounts of memory. Indexes containing 1M dense vectors (a small dataset in today’s world) will often require several GBs of memory
related reading
- TurboQuant: Redefining AI efficiency with extreme compressionresearch.google
- Lower memory footprint · facebookresearch/faiss Wiki · GitHubgithub.com
- Faiss: A library for efficient similarity search - Engineering at Metaengineering.fb.com
- Welcome to Faiss Documentation - Faiss documentationfaiss.ai
- TurboQuant: Redefining AI efficiency with extreme compressionresearch.google
- Announcing ScaNN: Efficient Vector Similarity Searchai.googleblog.com
- GitHub - facebookresearch/faiss: A library for efficient similarity search and clustering of dense vectors.github.com
- The vector database to build knowledgeable AI | Pineconepinecone.io
- Product quantization for vector searchnews.ycombinator.com
- Faiss indexes · facebookresearch/faiss Wiki · GitHubgithub.com
- [2504.19874] TurboQuant: Online Vector Quantization with Near-optimal Distortion Ratearxiv.org
- Indexing 1T vectors · facebookresearch/faiss Wiki · GitHubgithub.com