What are the storage requirements for large embeddings?

Storage Requirements for Large Embeddings Storing large embeddings primarily depends on three factors: vector dimensions, total quantity of vectors, and numerical precision. Each embedding is a high-dimensional vector (typically 100 to 4096 dimensions) representing data like text, images, or user behavior. For example, a 1536-dimensional float32 embedding requires 1536 * 4 bytes = 6,144 bytes (~6 KB) per vector. Storing 1 million such embeddings would need 6.1 GB, while 1 billion would require 6.1 TB. Using lower precision (e.g., float16 or 8-bit integers) cuts storage by 50–75%, but may impact model accuracy. Developers must balance precision, storage costs, and performance requirements for their use case.

Infrastructure and Storage Formats Choosing storage systems and file formats is critical for efficiency. Disk-based solutions (e.g., HDF5, Parquet) are cost-effective for static embeddings but introduce latency during retrieval. In-memory databases (e.g., Redis, FAISS-optimized indexes) speed up access but require significant RAM. For example, storing 100 million 768-dimensional float32 embeddings in RAM would need ~300 GB, which may demand distributed systems or sharding. Compression techniques like product quantization reduce footprint—FAISS uses this to shrink vectors by 4x–8x—but require trade-offs in retrieval accuracy. Developers should also consider metadata storage (e.g., IDs, timestamps), which can add 10–20% overhead depending on indexing needs.

Optimization Strategies Optimizing storage starts with analyzing usage patterns. For read-heavy applications (e.g., recommendation systems), caching frequently accessed embeddings in memory improves performance. Pruning unused or redundant embeddings (e.g., via clustering) reduces storage needs. Sparse embeddings, where most dimensions are zero, can leverage formats like CSR (Compressed Sparse Row) to save space. Hybrid approaches, such as storing high-precision embeddings on disk and lower-precision copies in memory, balance cost and speed. Tools like TensorFlow Extended (TFX) or PyTorch’s quantization utilities automate precision reduction. Finally, cloud storage (e.g., AWS S3) with tiered pricing for cold data can lower costs for archival embeddings. Each choice depends on the project’s scalability, latency, and budget constraints.