Equation 10 · Model Systems in 2035: Four Scenarios, Their Signals, and What Would Falsify Them
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the cache bytes. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.
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where W is the weight bytes touched and K the cache bytes. Pope and colleagues formalised the partitioning analysis behind this and showed how latency, throughput and cost trade against one another under different sharding layouts [ 12 ] . The mitigations are well documented and all attack the numerator or the batch: grouped-query attention shrinks the key–value cache by sharing key and value heads across query groups [ 14 ] ; PagedAttention removes fragmentation and over-reservation in cache allocation, reported at 2–4× throughput at equal latency [ 13 ] ; speculative decoding lets a cheap draft model propose tokens that the target verifies in parallel, provably without changing the sampled…
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where W is the weight bytes touched and K the cache bytes. Pope and colleagues formalised the partitioning analysis behind this and showed how latency, throughput and cost trade against one another under different sharding layouts [ 12 ] . The mitigations are well documented and all attack the numerator or the batch: grouped-query attention shrinks the key–value cache by sharing key and value heads across query groups [ 14 ] ; PagedAttention removes fragmentation and over-reservation in cache allocation, reported at 2–4× throughput at equal latency [ 13 ] ; speculative decoding lets a cheap draft model propose tokens that the target verifies in parallel, provably without changing the sampled distribution [ 15 ] .
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- [12] Efficiently Scaling Transformer Inference ↗
- [14] GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints ↗
- [13] Efficient Memory Management for Large Language Model Serving with PagedAttention ↗
- [15] Fast Inference from Transformers via Speculative Decoding ↗
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