# Embedding at the Speed of Light

> Source: <https://qdrant.tech/articles/embedding-at-the-speed-of-light/>
> Published: 2026-11-04 00:00:00+00:00

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.