Compressing Large Language Models with PCA Without Performance Loss

Magnus Bengtsson

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arXiv · 2508.04307

Compressing Large Language Models with PCA Without Performance Loss

Magnus Bengtsson

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Abstract

We demonstrate that Principal Component Analysis (PCA), when applied in a structured manner, either to polar-transformed images or segment-wise to token sequences, enables extreme compression of neural models without sacrificing performance. Across three case studies, we show that a one-layer classifier trained on PCA-compressed polar MNIST achieves over 98 percent accuracy using only 840 parameters. A two-layer transformer trained on 70-dimensional PCA-reduced MiniLM embeddings reaches 76.62 percent accuracy on the 20 Newsgroups dataset with just 81000 parameters. A decoder-only transformer generates coherent token sequences from 70-dimensional PCA embeddings while preserving over 97 percent cosine similarity with full MiniLM representations, using less than 17 percent of the parameter count of GPT-2. These results highlight PCA-based input compression as a general and effective strategy for aligning model capacity with information content, enabling lightweight architectures across multiple modalities.

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Magnus Bengtsson. 2025-08-06. Compressing Large Language Models with PCA Without Performance Loss. https://arxiv.org/abs/2508.04307

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