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
- Product quantization for vector searchnews.ycombinator.com
- The vector database to build knowledgeable AI | Pineconepinecone.io
- TurboQuant: Redefining AI efficiency with extreme compressionresearch.google
- Hierarchical Navigable Small Worlds (HNSW) | Pineconepinecone.io
- Faiss: A library for efficient similarity search - Engineering at Metaengineering.fb.com
- Lower memory footprint · facebookresearch/faiss Wiki · GitHubgithub.com
- Hierarchical Navigable Small Worlds (HNSW) | Pineconepinecone.io
- TurboQuant: Redefining AI efficiency with extreme compressionresearch.google
- turbopufferturbopuffer.com
- Nearest Neighbor Indexes for Similarity Search | Pineconepinecone.io
- Not All Vector Databases Are Made Equal | Towards Data Sciencetowardsdatascience.com
- Announcing ScaNN: Efficient Vector Similarity Searchai.googleblog.com