Summary
A new "Codebook Lossless LLM Compression" technique has been developed, offering 10-25% RAM reduction for large language models. This method exploits the observation that LLM weights often use fewer unique values than their fp16 representation, allowing for efficient bitwise packing. The compression achieves significant memory savings, though it involves a slight trade-off in inference speed.
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