Equation 14 · How AI Memory Systems and the Bandwidth Wall Actually Work
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which grows linearly in both batch size and sequence length, with the leading factor of two accounting for storing both keys and values [ 9 ] . That single equation is the whole mechanism: nothing about it is a design choice an inference engineer can simply decline. Extend the conversation, and l grows; serve more requests at once, and b grows; either way grows with it, and Hooper and colleagues note that at sufficiently long context lengths the KV cache — not the model’s weights — becomes the dominant consumer of memory during inference [ 9 ] .
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