# CUDA for AMD on Windows：基于 ZLUDA + HIP/ROCm 在 Windows 上运行 CUDA 应用的开源方案

- 来源：Hacker News 热门（buzzing.cc 中文翻译）
- 作者：chiassedu80
- 发布时间：2026-09-14 04:03
- AIHOT 分数：56
- AIHOT 链接：https://aihot.news/items/cmu091rl20cm0roryofzhghpg
- 原文链接：https://github.com/Speedstu/CUDA-for-AMD-Windows

## AI 摘要

GitHub 项目 CUDA-for-AMD-Windows 发布了可复现的技术栈，通过 ZLUDA v6-preview.69 + AMD HIP SDK 6.4 让 CUDA 面向的 Windows 应用在 AMD GPU 上运行。

## 正文

CUDA for AMD on Windows

WORKING REPRODUCIBLE STACK IS NOW UPLOADED.

Run CUDA-targeted Windows applications on AMD GPUs through ZLUDA + ROCm/HIP.

A reproducible Windows CUDA compatibility setup built around ZLUDA + AMD HIP/ROCm. It is intended for CUDA-facing compute applications, including workloads that use CUDA-enabled LibTorch.

[!IMPORTANT] Validated hardware is currently AMD Radeon RX 9060 XT (gfx1200) only. Other AMD GPUs are candidates, not guaranteed working devices. If you test another card, please open a GPU compatibility report, whether it works or fails.

Verified today

The public, upstream-only path has been tested without any private/recovered DLLs:

ZLUDA v6-preview.69 from the official ZLUDA release

AMD HIP SDK 6.4

LibTorch 2.3.0 + cu118

RX 9060 XT / gfx1200

nvcuda, cuBLAS, cuBLASLt, cuSPARSE and cuFFT all pass cuda_check

a real 2,216,347-parameter PPO network completed forward/inference, PPO learning and optimizer work on the CUDA-facing device

one clean validation iteration completed 65,536 timesteps using the runtime produced by this repository

That integration test used the same CUDA-facing LibTorch training workload that originally motivated this project. See docs/VALIDATION.md.

This does not mean every CUDA program or AI model works. CUDA API/library coverage is workload-dependent.

How it works

CUDA-targeted Windows application | ZLUDA | cuBLAS / cuSPARSE / cuFFT compatibility | rocBLAS / hipBLASLt / rocSPARSE / HIP | AMD GPU

Install

1. Install the AMD prerequisites

Install a current AMD GPU driver and the AMD HIP SDK for Windows including HIP Libraries.

The validated reference uses HIP SDK 6.4. Newer versions may work but should be treated as unverified until reported.

AMD Windows HIP SDK guide: https://rocm.docs.amd.com/projects/install-on-windows/en/docs-6.4.2/index.html

2. Clone and run the installer

git clone https://github.com/Speedstu/CUDA-for-AMD-Windows.git cd CUDA-for-AMD-Windows powershell -ExecutionPolicy Bypass -File .\scripts\install.ps1

install.ps1 will:

detect the AMD GPU and native gfxXXXX target;

verify the AMD driver/HIP SDK and required math libraries;

download the pinned official ZLUDA Windows build;

download LibTorch 2.3.0+cu118 (about 2.66 GB);

verify the downloaded SHA-256 hashes;

generate .runtime\runtime-config.json and .runtime\gpu-report.json;

run ZLUDA's cuda_check.exe against the installed AMD stack.

If you do not need LibTorch:

.\scripts\install.ps1 -SkipLibTorch

Run a CUDA-targeted application

.\scripts\run-zluda.ps1 -Program C:\path\to\app.exe

The launcher stages the required ZLUDA compatibility DLLs beside the target application and sets the HIP/ROCm runtime paths for that run.

You can also stage without launching:

.\scripts\stage-runtime.ps1 -TargetDir C:\path\to\your-app

Diagnose a machine

.\scripts\doctor.ps1 .\scripts\gpu-scan.ps1 .\scripts\test-runtime.ps1

The GPU scanner records the model, gfx architecture, driver and HIP information. It does not intentionally collect usernames, tokens or user files.

Example on the validated machine:

AMD Radeon RX 9060 XT -> gfx1200 -> RDNA4 -> validated-reference

Current GPU status

GPU Target Project status

Radeon RX 9060 XT gfx1200 ✅ validated reference

The scanner recognizes other Windows HIP architecture families and marks them as unverified candidates rather than claiming support. Detection is not proof that a workload runs.

AMD's current Windows hardware table: https://rocm.docs.amd.com/projects/install-on-windows/en/latest/reference/system-requirements.html

Runtime coverage on the validated setup

Current upstream runtime check:

CUDA-facing component Result

CUDA driver / nvcuda ✅

cuBLAS ✅ via rocBLAS

cuBLASLt ✅ via hipBLASLt

cuSPARSE ✅ via rocSPARSE

cuFFT ✅

cuDNN ⚠️ unavailable with the validated stable Windows HIP SDK

The stable Windows HIP SDK does not ship the full ROCm AI-library stack such as MIOpen, so convolution-heavy software that requires cuDNN can need a newer/nightly HIP stack or additional work. Dense/GEMM-heavy LibTorch training does not necessarily require cuDNN; the validated PPO workload completed without it.

Performance

A controlled 2026-09-13 A/B ran 10 iterations per runtime on the same RX 9060 XT PPO workload. After discarding the first iteration of each trial as warmup, the public upstream path reached 13,278 median overall SPS versus 12,876 for the recovered custom overlay. In this workload the custom overlay was about 3.03% slower, so upstream remains the default.

Historical tuned runs used a different training configuration and reached roughly 70k–109k overall steps/s. See docs/BENCHMARKS.md for methodology and raw data.

Optional historical custom overlay

The original development environment also experimented with a custom cuBLAS/cuBLASLt/HIP overlay. It is not required for the validated public path and, based on the controlled A/B above, is not currently a performance win for the reference PPO workload.

The recovered DLLs remain fingerprinted in manifests/recovered-artifacts.sha256. They are not published as binary blobs because the original custom wrapper source/provenance is incomplete and the recovered HIP runtime contains third-party AMD binaries. See docs/CUSTOM_OVERLAY.md.

Found a bug or tested another GPU?

Please publish an issue. Failed tests are useful too.

.\scripts\gpu-scan.ps1 -OutputPath .\gpu-report.json .\scripts\test-runtime.ps1

Then open a GPU compatibility report and include the application, result and first useful error/output.

Repository layout

scripts/ install, diagnostics, scanner, staging and launcher manifests/ pinned versions, hashes and GPU architecture metadata docs/ validation, architecture, benchmarks and troubleshooting examples/ integration/reference snippets .runtime/ generated dependencies and reports; ignored by Git local-artifacts/ local archival files; ignored by Git

Limitations

Only RX 9060 XT / gfx1200 is currently validated by this project.

ZLUDA is not a complete CUDA implementation.

Windows exposes only a subset of the full ROCm ecosystem.

cuDNN/MIOpen is not available in the validated stable HIP SDK path.

NCCL, TensorRT, unsupported PTX behavior and some custom CUDA extensions may fail.

ZLUDA_CC=8.6 is a CUDA-facing compatibility value, not the AMD GPU architecture.

License and third-party software

Project-owned scripts and documentation are MIT licensed. ZLUDA, AMD ROCm/HIP, NVIDIA CUDA components and PyTorch/LibTorch retain their own upstream licenses. See THIRD_PARTY_NOTICES.md.