How do trillion-scale unsupervised learning systems handle embedding storage and retrieval?

They use distributed vector stores, quantization, and hierarchical indexing to manage massive embedding spaces efficiently.

At trillion-scale, storing embeddings in raw float32 format is infeasible due to memory constraints. Systems rely on product quantization (PQ), scalar quantization, and compressed vector representations. Retrieval uses multi-stage pipelines: coarse filtering (ANN like HNSW or IVF), followed by fine reranking. Distributed sharding ensures embeddings are partitioned across nodes, while replication ensures fault tolerance. Systems like FAISS-based clusters or proprietary vector engines are optimized for GPU + CPU hybrid search pipelines.