Equation 16 · Dense, Sparse, and Distilled: Comparing Approaches to Frontier Model Capacity
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What sparsity does not break is M . Every expert must be resident somewhere at serving time even though any given token touches one. Since decoding is bound by memory traffic rather than arithmetic — Gholami and colleagues report peak server FLOPS scaling at roughly 3.0× every two years against DRAM and interconnect bandwidth at about 1.6× and 1.4× [ 9 ] — a sparse model’s advantage is real in training and much more conditional in serving. It buys quality per training FLOP and per activated parameter; it does not buy memory.
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