Linear layers hold most of a transformer's parameters. We replace each linear layer with one that stores $K$ out of $mn$ two-dimensional DCT coefficients per weight matrix and reconstructs the full matrix through an inverse DCT at every forward pass; the $K$ coefficients are the trainable parameters. A 4-layer, 128-dim transformer trained from scratch on character-level Shakespeare reaches validation loss $1.604$ at $K = mn/2$, against $1.580$ for a standard dense baseline -- a gap of $+0.024$ at half the trainable parameter count, within the terminal-epoch variation of the dense run. A rank-48 LoRA factorization at the same trainable parameter count reaches only $1.801$ ($+0.221$). The structural advantage of sparse-coefficient over low-rank parameterizations at matched $K$ is qualitative. We identify rank flexibility as the mechanism. A random orthonormal basis matches the DCT within noise at $K = mn/2$, and a compression sweep through $K = mn/10$ and $K = mn/20$ shows that subspaces that can host high-rank matrices keep the loss low, while subspaces that flatten into a low-rank block (zigzag-selection variants) converge onto the observed stable rank \emph{and} the loss line of the rank-48 LoRA reference in lock-step. Among these orthonormal bases, the DCT is preferred because its separable fast transform admits a fused reconstruction kernel: the materialized weight matrix never leaves on-chip memory, so the parameter saving translates into a bandwidth saving as well.

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