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Large pre-trained word embeddings are a cornerstone of modern NLP, but their memory footprint is a real deployment bottleneck. A standard 300-dimensional GloVe vocabulary of 2 million tokens consumes ~2.4 GB in float32. On mobile devices, edge hardware, or in multi-model serving environments, this...