Developers within the open-source community have uncovered a bottleneck: certain NVIDIA GeForce graphics cards exhibit restricted instruction issue rates for their Tensor Cores during specific mixed-precision calculations. By adjusting configurations during the GPU's initialization phase, developers can substantially enhance the throughput of these constrained computational pathways. This breakthrough eliminates the need for hardware upgrades, BIOS flashing, or official driver alterations. To date, performance enhancements have been successfully implemented across a range of graphics cards from NVIDIA's RTX 20, 30, and 40 series. Remarkably, the RTX 4090 has witnessed its Tensor Core computational throughput nearly double, while several models in the RTX 30 series have demonstrated performance improvements approaching 100%. The RTX 2080 Ti has also experienced notable gains. Consequently, certain practical AI tasks have undergone significant acceleration. These unlocking solutions have been rigorously validated in a Linux environment, with preliminary success also reported in Windows 11.