Embeddings scale with data size by increasing the computational and storage resources required for training and inference. As the dataset grows larger, the model generating embeddings may need more parameters or processing power to learn the relationships between data points. In general, more data leads to better quality embeddings, as the model can learn richer representations. However, the scalability of embeddings is constrained by the hardware resources available, such as GPU memory and storage.
For example, training word embeddings on a large corpus of text requires significant computational power, and as the data grows, the model may need to be trained in a distributed environment. Similarly, as the number of data points increases, the storage requirements for storing the embeddings also grow. Techniques like batching, distributed training, and the use of specialized hardware (e.g., TPUs) can help scale embedding models to handle large datasets.
Embedding models can also use dimensionality reduction or quantization to help scale with increasing data size. Additionally, efficient indexing techniques like Approximate Nearest Neighbor (ANN) can be used to handle large embedding spaces and enable fast retrieval even as the data grows in size.