Running CUDA Workloads on AMD GPUs Under Windows: Architecture, Setup, and Alternatives

The dominant position of NVIDIA in artificial intelligence and high-performance computing is largely built on CUDA (Compute Unified Device Architecture). For years, developers wishing to run machine learning workloads, PyTorch models, or LLM inference engines were constrained to NVIDIA hardware. However, recent developments in binary translation layers, compiler toolchains, and AMD's ROCm ecosystem are changing this paradigm. Running CUDA applications on AMD Radeon GPUs within a Windows environment has transitioned from an experimental science project to a practical consideration for developers seeking high VRAM per dollar.

In this deep dive, we examine the underlying architecture of CUDA compatibility layers on Windows, walk through setup workflows for AI inference, evaluate performance trade-offs against native hardware, and explore how unified API platforms like n1n.ai provide a zero-friction fallback when local hardware translation hits driver limitations.

1. The Technology Stack Behind CUDA Emulation on AMD

To understand how CUDA runs on AMD silicon under Windows, one must analyze the software abstractions separating application code from the physical graphics processing unit (GPU).

+-------------------------------------------------------------+
|        User Application (PyTorch, llama.cpp, vLLM)          |
+-------------------------------------------------------------+
                              |
                     [ CUDA Runtime API ]
                              |
       +----------------------+----------------------+
       |                                             |
[ ZLuda (DLL Intercept) ]             [ SCALE Compiler (LLVM IR) ]
       |                                             |
       +----------------------+----------------------+
                              |
                  [ AMD HIP / ROCm Runtime ]
                              |
               [ AMD KFD Driver (Windows KMD) ]
                              |
             +----------------------------------+
             | AMD Radeon GPU (RDNA2 / RDNA3)   |
             +----------------------------------+

ZLuda: Dynamic Library Interception

ZLuda operates as an open-source drop-in replacement for NVIDIA CUDA libraries. Instead of rewriting or recompiling source code, ZLuda intercepts calls to nvcuda.dll and cudart64_*.dll. When a CUDA application requests execution of a kernel, ZLuda translates the CUDA Driver API calls into equivalent AMD HIP (Heterogeneous-computing Interface for Portability) function calls at runtime.

AMD HIP SDK for Windows

AMD's official answer to CUDA is HIP. HIP provides a C++ dialect and runtime that closely mirrors CUDA APIs. On Linux, ROCm offers complete driver integration. On Windows, AMD provides the HIP SDK, allowing C++ developers to target both AMD and NVIDIA devices using unified source code.

Compiler Translation (SCALE & LLVM IR)

Projects like SCALE take a compilation-based approach. Rather than runtime interception, SCALE acts as a CUDA C++ compiler driver that outputs native GCN/RDNA instruction set architecture (ISA) machine code via an LLVM IR transformation pipeline. This eliminates runtime translation overhead for supported language constructs.

2. Setting Up CUDA for AMD on Windows: Practical Implementation

Running CUDA binaries on Windows with AMD hardware requires configuring environment variables and pointing application dynamic link libraries (DLLs) to ZLuda binary paths.

Step 1: Drivers and Prerequisites

1. Install the latest AMD Software: Adrenalin Edition driver (matching your RDNA2/RDNA3 card like RX 6800, RX 7900 XTX).
2. Install the AMD HIP SDK for Windows (v5.7 or higher).
3. Ensure Git and Python (3.10+) are configured in your system PATH.

Step 2: Extracting ZLuda Binaries

Download the prebuilt ZLuda Windows release binaries and extract them to a permanent directory (e.g., C:\Program Files\zluda).

Set the system environment variables using PowerShell:

# Set Environment Variables for ZLuda execution
[System.Environment]::SetEnvironmentVariable('HIP_VISIBLE_DEVICES', '0', 'User')
[System.Environment]::SetEnvironmentVariable('ZLUDA_LOG', '1', 'User')

# Append ZLuda to the front of PATH so its DLLs take precedence
$env:Path = "C:\Program Files\zluda;" + $env:Path
[System.Environment]::SetEnvironmentVariable('Path', $env:Path, 'User')

Step 3: Verifying PyTorch via CUDA API Interception

Create a test script check_cuda_amd.py to verify whether PyTorch detects the AMD card as a CUDA device through ZLuda layer interception:

import sys
import torch

def verify_amd_cuda():
    print(f"Python Version: \{sys.version\}")
    print(f"PyTorch Version: \{torch.__version__\}")
    print(f"CUDA Available to PyTorch: \{torch.cuda.is_available()\}")
    
    if torch.cuda.is_available():
        device_count = torch.cuda.device_count()
        print(f"Device Count: \{device_count\}")
        device_name = torch.cuda.get_device_name(0)
        print(f"Primary Device Name: \{device_name\}")
        
        # Test basic tensor allocation and GEMM operation
        try:
            x = torch.randn(2048, 2048, device="cuda