EntroLLM applies tensor-level mixed quantization to reduce weight entropy then uses Huffman coding for up to 65% storage savings and faster inference on memory-limited edge devices without retraining.
Huffman encoding [26] optimally assigns shorter codes to frequent symbols and is the core of the beyond- quantization compression framework in our work
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EntroLLM: Entropy Encoded Weight Compression for Efficient Large Language Model Inference on Edge Devices
EntroLLM applies tensor-level mixed quantization to reduce weight entropy then uses Huffman coding for up to 65% storage savings and faster inference on memory-limited edge devices without retraining.