Mixture-of-Experts (MoE) routing collapses in parameter-efficient fine-tuning: on LLaMA-3 8B with MixLoRA, a single expert absorbs 82.5% of tokens. Prior MoE-PEFT remedies intervene at the output: routing reformulations, output-similarity penalties, or shared-expert capacity. All leave each routed expert’s input view identical. We propose SIMPA (Sparse-Input Magnitude Partitioning Adaptation), which restricts what experts *see* rather than what they produce: a magnitude-thresholded input drives a sparse routed-LoRA branch, a parallel dense LoRA branch (adapting DeepSeekMoE’s (Dai et al., 2024) shared-expert topology to PEFT) provides an information safeguard, and a column-wise gate fuses the two. At a matched 2.9% trainable-parameter budget, SIMPA leads compared baselines on reasoning (86.2% on seven LLaMA-3 8B tasks, +3.9 over MixDoRA), code and arithmetic (38.4 HumanEval Pass@1 and 59.4 GSM8K, matching full fine-tuning under identical data and compute), and NLU (88.7 GLUE / 77.7 SuperGLUE on T5-base); against direct MoE-PEFT counterparts HydraLoRA and MoLoRA at matched setup, SIMPA leads HumanEval by +14.2 and +13.6 and GSM8K by +2.1 and +2.8. Direct measurement returns an expert-input Jaccard of 0.184 versus MixLoRA’s 1.000, a 5.4× reduction. The same block-aligned mask drives a Triton kernel measuring 1.46× end-to-end inference speedup on A100. Closed-form partitioning and retention bounds characterize the mechanism, and a tail-index-driven closed form predicts the optimal sparsity p^* from the pretrained backbone. The mechanism, its predicted optimum, and its bound all transfer to a held-out Qwen2.5-7B backbone. Code: https://github.com/triquang26/simpa.