Summary
Fahd Mirza examines ZLUDA, the translation layer that allows unmodified CUDA applications to run on AMD GPUs, and focuses specifically on a GitHub project called “CUDA for AMD on Windows” by developer speeds2 — a Windows-native package that wires together ZLUDA, AMD’s HIP SDK, and ROCm into a single reproducible installation path.
The video builds a clear mental model of the four-layer stack: a CUDA app at the top communicates with ZLUDA (which impersonates the CUDA runtime), which maps library calls such as CUBLAS to their ROCm equivalents (ROCm BLAS), which in turn run on the AMD GPU hardware. The practical installation process is laid out in five steps: install the AMD driver and HIP SDK 6.4, clone the repo, run an installer script that auto-detects the GPU and validates hashes, run a diagnostic script, and point a launcher at any target CUDA application. The author of the project reported performance landing approximately 3% slower than native Nvidia on a reinforcement learning training workload — a result Mirza calls genuinely promising.
Mirza is candid about current limitations: only one GPU (the RX 9060 XT) has been validated by the project author, QDNN is absent from the stable Windows HIP SDK meaning convolution-heavy image models may fail, and ZLUDA remains an incomplete CUDA implementation. The video frames the project as an early but meaningful step in eroding Nvidia’s CUDA moat for Windows users who own AMD hardware.
📺 Source: Fahd Mirza · Published September 14, 2026
🏷️ Format: Deep Dive
