If you port CUDA code to AMD, will it run just as fast?

Suppose you found a Radeon configuration with generous memory within budget, but the installation guide for the local image-processing app you need mentions only CUDA. The next natural question is: if the code is moved to AMD, will it run just as fast? Converting the source to HIP³ and seeing a GPU⁴ name in the log does not mean the port is finished. Getting the program to launch, producing correct output, staying stable over a long session, and finishing in a time comparable to the CUDA configuration are separate things to verify. This guide walks through checking the app's official path and comparing the actual task before buying that Radeon.

Before looking at porting tools, find the execution path the app officially supports. Before buying the GPU, check the project's installation guide, release files, issues, and support list for a ROCm/HIP build. “CUDA support” alone does not imply AMD support. HIP provides a GPU programming API⁵ similar to CUDA, while HIPIFY helps convert some source calls to corresponding HIP forms. The work and validation required differ greatly depending on whether the app publishes a packaged ROCm⁶ build or expects users to port the source themselves. For a buyer, checking first for an official binary or installation path is a practical way to reduce purchase risk.

Imagine that the local image tool ships only a CUDA build, and you want to run a similar setup on Radeon. Running HIPIFY over the repository's source does not by itself produce a finished AMD app. The project determines how GPU work is divided into work items, where inputs and intermediate results live, and when memory is copied or reused. If it calls CUDA libraries such as cuBLAS, you also need to check the corresponding library and function behavior. Even if an automatic converter changes function names, it does not automatically optimize work-group sizes, data layout, synchronization points, or library algorithms that affect performance. That is why a successful compile alone says nothing conclusive about speed.

Check correctness separately first. Process the same input file on both systems and see whether the outputs fall within the application's tolerance. Floating-point operations can differ in the last few digits depending on execution order or kernel implementation. Decide based on the application's purpose whether bit-for-bit identical output is required or a specified tolerance is sufficient. For image generation, even with the same seed and settings, do not assume every GPU will produce identical pixels; compare against the application's quality criteria and failure modes. If the result is wrong at this stage, a speed comparison has no value.

Next, check stability. Processing one small input does not mean the app is safe through a long session. Save a representative image, process that same file repeatedly, then close and restart the app to see whether the workflow remains reproducible. Do not jump to a much larger model or image size at once; increase gradually to the resolution you normally use. Watch for memory use that keeps accumulating, errors that appear only with larger inputs, and whether resources are released after the app exits. Fixing the model file, precision, image resolution, and other open apps to match your real use helps distinguish a one-off success from a repeatable session.

Only then compare performance. Even when they use the same PyTorch⁷ API names or similar source code, NVIDIA CUDA and AMD ROCm run the GPU through different runtimes, libraries, and kernel implementations. Similar line counts or a short conversion do not prove that execution time will match. Write down the comparison conditions before buying new hardware. Keep the image input, output resolution and processing steps, app version, and quality criteria the same. If one side has to offload⁸ work to the CPU⁹ because of memory limits, record that too and decide whether it represents the configuration you would actually use.

Also specify what the timing includes. Loading and preparing the model can take a different amount of time from processing an image once the app is ready. What matters to a user may be the time from pressing Run until the output file is saved. Record preparation, the first image, repeated images, and end-to-end completion including saving separately. Processing the same image more than once helps show whether a configuration is quick only in a cached session or remains useful when the model must be prepared each time. State which case you measured so the number can be related to the experience after purchase.

A fast configuration in a published benchmark still cannot make the purchase decision for you. If the app version, GPU memory behavior, or output-quality criteria differ, you cannot apply that number directly to your work. Driver and ROCm versions can also affect results, and one app may stop when memory runs out while another moves some work to the CPU. Test with settings that match the image tasks you actually intend to run for the comparison to inform your purchase.

If you are unsure what to test, start with the app's execution log. Seeing a HIP runtime¹⁰ or AMD GPU in the log is a clue about which path ran. It may indicate that device discovery or runtime initialization succeeded, but it does not guarantee that the entire app works correctly or makes effective use of the GPU. Check detailed logs for model initialization, memory allocation, kernel execution, and output saving. If the program has no official ROCm build, remember that a community-converted executable has different maintenance and bug-reporting responsibilities.

In your comparison notes, record the GPU model, OS build, driver and ROCm versions, app release, image input, output resolution and processing steps, and quality criteria. Keep the settings file, output image, and logs. If you update the app or model during testing, mark it as a new run. When alternating between an existing CUDA PC and a Radeon candidate, use the same file and settings, start a fresh session, and separate model initialization, the first run, repeated runs, and saving the result. This shows whether the difference is limited to initial loading or persists during repeated work. Keep reproducible error details and output quality too, so you can review stability alongside speed. Note power limits or different background workloads so you can check whether the conditions really match.

When timing, do not stop at the first progress indicator; define the interval through the app's completion signal and saved output. In apps that queue GPU work asynchronously, the button press and actual completion may occur at different times, so also check the app's completion log or output-file timestamp. Use the same preparation state on both computers and review variation across repeated runs. This reduces the chance that a one-off cache effect or background task drives the purchase decision.

The useful purchase question is not whether source code can be converted, but whether the exact app you plan to use has a verifiable AMD path. If an official ROCm build exists, match its documented GPU, OS, driver, ROCm, and framework combination, then repeat a representative image task through saving and restarting. If only source is available, review the changes HIPIFY made, verify correctness, stability, and performance, and decide who will maintain the AMD environment. A ported app launching once is no reason to expect the same speed as the CUDA build. If there is no official AMD path and you do not plan to own the porting and validation work, it is practical to hold off on buying that GPU for this app.

For background, see the CUDA video by Andeol Gonghak. For applying porting steps and API differences to a real project, consult AMD's HIP porting guide and HIPIFY documentation independently of the video.