The storage requirements for embeddings depend on the dimensionality of the embeddings, the number of data points, and the data type being represented (e.g., text, images). Embeddings are typically stored as vectors of floating-point numbers, and each vector consumes memory proportional to its dimensionality. For instance, a 300-dimensional word embedding would require 1,200 bytes (assuming 4 bytes per float). The total storage requirement increases with the number of data points and dimensions.
In practice, embeddings are often stored in binary formats (e.g., NumPy arrays, or serialized formats like Protobuf or Apache Parquet) to optimize storage and retrieval efficiency. For large-scale systems, embeddings are stored in distributed storage solutions, such as cloud object storage (e.g., AWS S3) or specialized databases like vector databases. These systems handle large-scale embeddings efficiently, enabling fast access and retrieval.
In general, organizations need to balance the need for high-dimensional, high-quality embeddings with the cost of storage and retrieval speed. Storage optimization techniques like quantization (reducing precision) or dimensionality reduction (using techniques like PCA) can help reduce storage requirements.