We present a sparsity-aware FPGA implementation of an adaptive Transformer-based localization accelerator for 5G massive MIMO targeting sub-10\,ms real-time positioning. The architecture exploits propagation characteristics, where beam-delay channel representations exhibit sparsity, enabling a row-wise skipping mechanism that removes low-energy beam components with minimal control overhead. Transformer computations are mapped onto a heterogeneous vector processing engine with parallel processing elements and adder trees, using mixed input- and output-stationary dataflow execution for efficient matrix computation and reduced data movement. Environment-dependent processing is supported through a lightweight runtime model-switching mechanism, where temporally filtered outputs of a single-layer perceptron router enable selection between specialized models with reduced latency. Implemented on a Xilinx Zynq UltraScale+ FPGA and evaluated on real-world massive MIMO measurements, the design achieves up to 65\% row sparsity, yielding peak computational speedups of approximately 2x while limiting the average localization accuracy degradation to below 10\%, relative to the fixed-point baseline model. The accelerator attains below 1.15\,m localization accuracy across scenarios, with inference latency of 0.51-2.11\,ms and throughput of up to 1961 positions/s. These results demonstrate that propagation-aware sparsity, mixed dataflow execution, and efficient runtime model switching enable a scalable and low-latency hardware realization of adaptive Transformer-based localization for real-time 5G systems.

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