Equation 13 · Dense, Sparse, and Distilled: Comparing Approaches to Frontier Model Capacity
What does this equation mean?
Read the formula alongside the article passage below. Each part has a deeper page with its role in the equation, the supporting passage and nearby citations.
This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.
Read it piece by piece
Symbol k
k is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.
How to interpret it
Read this expression with the definitions, units, and assumptions supplied by the article.
What the article says around this equation
Mixture-of-experts breaks the identity = N . Shazeer and colleagues introduced the sparsely-gated MoE layer, in which a learned gate selects a small subset of expert sub-networks per example, allowing parameter counts far beyond what could be densely activated at the same compute [ 4 ] . Fedus, Zoph, and Shazeer simplified it decisively: the Switch layer routes each token to exactly one expert rather than the top- k , which reduced routing computation and communication cost while preserving quality, and they scaled the approach to trillion-parameter models [ 5 ] .
Sources cited in the surrounding passage
- [4] Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer ↗
- [5] Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity ↗
These citations give research context. Read each source to check which claims it supports.
Return to Dense, Sparse, and Distilled: Comparing Approaches to Frontier Model Capacity