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Hkv⋅dhH_{\mathrm{kv}} \cdot d_h

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Peer-reviewed evidence, three different levers. DeepSeek-V2 replaces full multi-head keys and values with a low-rank latent projection — reducing HkvH_{\mathrm{kv}} ⋅\cdot dhd_h to a much smaller compressed dimension — and reports a 93.3 percent reduction in KV-cache size relative to the company’s own prior 67-billion-parameter dense model, while extending supported context to 128,000 tokens [ 15 ] . That shrinks the per-token cost of the equation above. StreamingLLM instead shrinks effective S : it keeps only a small window of recent tokens plus a handful of initial “attention sink” tokens, and reports enabling stable generation over sequences up to four million tokens with up to a 22.2-times…

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HkvH_{\mathrm{kv}}

Symbol H_kv

HkH_kv is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

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Hkv⋅dhH_{\mathrm{kv}} \cdot d_h

Equation 17 · Semiconductors

AI Memory Systems and the Bandwidth Wall in 2035: Scenarios, Signals, and Falsifiable Predictions

This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text.

Peer-reviewed evidence, three different levers. DeepSeek-V2 replaces full multi-head keys and values with a low-rank latent projection — reducing HkvH_{\mathrm{kv}} ⋅\cdot dhd_h to a much smaller compressed dimension — and reports a 93.3 percent reduction in KV-cache size relative to the company’s own prior 67-billion-parameter dense model, while extending supported context to 128,000 tokens [ 15 ] . That shrinks the per-token cost of the equation above. StreamingLLM instead shrinks effective S : it keeps only a small window of recent tokens plus a handful of initial “attention sink” tokens, and reports enabling stable generation over sequences up to four million tokens with up to a 22.2-times…

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