Equation 6 · How AI Memory Systems and the Bandwidth Wall Actually Work
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the number of layers. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.
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Hooper and colleagues, working on KV cache compression for very long contexts, state the resulting footprint precisely: for a model with n layers and h attention heads of dimension d , stored using e bytes per element, the KV cache size for batch size b and sequence length l is
Sources cited in the article section
- [9] KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization ↗
- [10] Efficiently Scaling Transformer Inference ↗
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