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One of the best ways 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 (frequent for "smaller" LLM embedding models) occupies 94.Forty nine MB of system reminiscence, which is comparatively low for contemporary personal computer systems and may fit within Free DeepSeek r1 utilization tiers of cloud VMs.