Equation 13 · AI Memory Systems and the Bandwidth Wall in 2035: Scenarios, Signals, and Falsifiable Predictions
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This equation states an equality: the expressions on both sides have the same value under the article’s assumptions. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.
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Symbol M_KV
V is part of the quantity the equation computes from the expression on the right.
Symbol H_kv
v is an input to the expression that computes the quantity on the left.
=
The expressions on both sides represent the same quantity under the stated assumptions.
See an illustrated explanation →subscript
The lower label selects a particular version, component, or indexed member of the quantity. For example, x₀ and xₜ can be values at different positions.
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What the article says around this equation
Fact. The memory a transformer must hold for one in-flight generation — its key-value cache — grows linearly in exactly the variables that make serving expensive: sequence length and batch size. For a model with L layers, key-value heads, head dimension , sequence length S , batch size B , and p bytes stored per element, the cache occupies approximately . bytes, the leading factor of two accounting for keys and values together. That equation is worth writing out because it exposes the one real lever every mitigation below actually pulls: none of them escapes linear growth in S and B . Each instead shrinks one of the other factors — most consequentially…
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Fact. The memory a transformer must hold for one in-flight generation — its key-value cache — grows linearly in exactly the variables that make serving expensive: sequence length and batch size. For a model with L layers, key-value heads, head dimension , sequence length S , batch size B , and p bytes stored per element, the cache occupies approximately . bytes, the leading factor of two accounting for keys and values together. That equation is worth writing out because it exposes the one real lever every mitigation below actually pulls: none of them escapes linear growth in S and B . Each instead shrinks one of the other factors — most consequentially , the number of key-value heads actually stored.
Sources cited in the article section
- [15] DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model ↗
- [14] Efficient Streaming Language Models with Attention Sinks ↗
- [16] H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models ↗
These citations give research context. Read each source to check which claims it supports.