How do embeddings reduce memory usage?

Embeddings reduce memory usage by converting high-dimensional, sparse data into lower-dimensional, dense representations. For example, in natural language processing (NLP), words are often represented as one-hot encoded vectors where each word corresponds to a unique position in a large vocabulary-sized array (e.g., 10,000 dimensions). Storing these sparse vectors requires significant memory because each vector is mostly zeros. An embedding layer maps these high-dimensional vectors into a smaller, fixed-size space (e.g., 300 dimensions) where each dimension captures a learned feature of the word. This reduces memory consumption by orders of magnitude—instead of storing 10,000 floats per word, you store 300, cutting memory usage by over 30x.

Embeddings also compress information more efficiently by capturing semantic relationships. For instance, in a recommendation system, user and item IDs might initially be represented as one-hot encoded IDs. A system with 1 million users and 100,000 items would require a 1.1-million-dimensional vector per interaction, which is impractical. Using 50-dimensional embeddings reduces this to 50 values per user and item. Additionally, embeddings group similar entities (e.g., users with similar preferences) closer in the vector space, allowing the model to generalize better with fewer parameters. This eliminates redundant storage of separate features and leverages shared patterns, further optimizing memory.

In practice, embeddings are implemented as lookup tables (matrices) where each row corresponds to an entity (e.g., a word or user). For a model with a 10,000-word vocabulary and 300-dimensional embeddings, the embedding matrix is 10,000 × 300—far smaller than a 10,000 × 10,000 one-hot matrix. During training, these embeddings are updated to minimize loss, refining their information density. This approach scales well: doubling the vocabulary adds only 300 new parameters per word, not 10,000. By focusing on dense, learned representations, embeddings drastically cut memory while preserving critical information for tasks like classification or clustering.