Equation 10 · AI Memory Systems and the Bandwidth Wall in 2035: Scenarios, Signals, and Falsifiable Predictions
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the sequence length. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.
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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
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 ↗
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