Embedding a large corpus is usually the slowest part of building a vector search pipeline: transformer-based models capture semantics very well, but they are heavy on CPU, so you either wait days for the embeddings or pay for a costly GPU.
Static embedding models avoid that cost by skipping the neural network altogether. A static model is a lookup table that associates each token in the model’s vocabulary with an embedding: to embed a text, you tokenize it, look up the vector of each token, and average them. With potion-base-8M, this let us embed 32.9 million passages on a laptop CPU in 35 minutes, where a transformer-based model would have needed about 53 days. The tradeoff is retrieval quality, because without layers the tokens never influence each other.