The "CUDA for AMD on Windows" project by GitHub user Speedstu provides a reproducible stack to run unmodified CUDA applications on AMD hardware by utilizing ZLUDA 6 as a translation layer. This stack functions by intercepting CUDA API calls—such as nvcuda, cuBLAS, cuSPARSE, and cuFFT—and forwarding them to AMD's HIP SDK via libraries like rocBLAS and rocSPARSE. Validated on an AMD Radeon RX 9060 XT (RDNA4, gfx1200) using the AMD HIP SDK 6.4 and LibTorch 2.3.0+cu118, the setup successfully completed a reinforcement learning task involving a 2,216,347-parameter PPO network over 65,536 timesteps. While the translation layer effectively handles dense GEMM-heavy workloads, it currently lacks cuDNN support due to the absence of MIOpen in the stable Windows HIP SDK. Consequently, the stack is optimized for dense linear algebra rather than convolution-heavy tasks or multi-GPU workloads requiring NCCL or TensorRT. The implementation is driven by a PowerShell-based installer that automates GPU detection, dependency verification via SHA-256 hashes, and runtime configuration, transforming ZLUDA from a manual assembly process into a scriptable workflow for Windows users.