The best way to make use of Text Embeddings Portably is With Parquet and Polars. Using numpy and my Magic card embeddings, a 2D matrix of 32,254 float32 embeddings at a dimensionality of 768D (common for "smaller" LLM embedding fashions) occupies 94.Forty nine MB of system memory, which is relatively low for contemporary personal computer systems and can match inside free usage tiers of cloud VMs.