A reproducible GPU benchmarking lab that compares FP16 vs FP32 training on MNIST using PyTorch, CuPy, and Nsight profiling tools. This project blends performance engineering with cinematic storytelling—featuring NVTX-tagged training loops, fused CuPy kernels, and a profiler-driven README that narrates the GPU’s inner workings frame by frame.

🎬 Explore GPU training efficiency with FP32 vs FP16 in this modular lab, utilizing Tensor Core acceleration for deep learning insights.

The goal of this reposotory is create a downcasting the Fashion MNIST model from FP32 to FP16 using pytorch

AI Performance Benchmark Tool. Unifies CPU, GPU (Metal), and NPU (Neural Engine) tests in GFLOPS and TOPS.