A fundamental challenge in quantum computing is efficiently loading classical data onto quantum computers. Quantum Random Access Memory (QRAM) offers a solution, acting as a universal architecture designed to implement oracles effectively. This article introduces QRAMsim, a novel framework for simulating, analyzing, and optimizing large-scale QRAMs. We first propose a basis vector tracking theorem to leverage the natural sparsity in the Hilbert space of QRAM, significantly reducing the space and time complexity for modeling QRAM. Furthermore, we develop a high-performance simulator integrated with a mapping algorithm to efficiently deploy QRAM on quantum devices. Experiments show that we can achieve \(8.2\times \) fidelity improvement compared to the conventional qubit mapping method [27], and achieve \(10^8\times \) simulation speedup compared to Qiskit. QRAMsim is publicly available on ( https://github.com/Chenning-Tao/QRAM_simulator ). To the best of our knowledge, this is the first open-source high-performance QRAM simulation framework.