Radiotherapy is a cancer treatment that uses high-energy X-rays to kill cancer cells. A major challenge in treating cancer is the heterogeneity of the disease. Preclinical models are important translational tools to study this heterogeneity's impact on treatment efficacy, but there have been many challenges in scaling preclinical radiotherapy due to the scale of small animals and lower orthovoltage energy range (100-500 kV) used for treatments. One major challenge is accurate dose calculations. Monte Carlo (MC) methods, a repeated random sampling technique, is a promising approach. Two computational approaches employing these MC methods, high-performance computing (HPC-MC) and graphics processing unit computing (GPU-MC), are studied for their applications in preclinical radiotherapy. HPC-MC, running a full MC approach, will be a vital validation tool for faster dose calculation algorithms, especially in higher order treatment planning and inverse planning. GPU-MC, running a fast MC approach, will be an important tool for treatment planning software and inverse planning optimization. Ultimately, HPU-MC and GPU-MC will help usher a much-needed paradigm shift in preclinical radiotherapy from dose to a patient to dose distributions within a patient.