How to Install AMD ROCm on Windows and Linux: Step-by-Step Guide and Compatibility

If you work with IA, HPC or scientific computing and you have a Radeon GPU or a Ryzen APU, you've probably heard of ROCm. AMD's platform aims to be the open foundation for programming and accelerating GPU tasks., with a special focus on Linux and, in a preliminary version, also on Windows for certain cases. Here, you'll find, step by step, what you need to know to install it and get it up and running.

First of all, it is worth anticipating something that often goes unnoticed in marketing: Installation success and performance depend heavily on the hardware, of the operating system and the exact version of ROCmIn the following sections we gather all the key information (official installation methods, compatibility, Tricks performance, Windows status, and real-life experiences) so you don't get caught off guard.

What is AMD ROCm and why it matters

ROCm is AMD's software stack for high-performance computing (HPC) and machine learning. Essentially, integrates CPU and GPU to accelerate intensive workloads, allowing kernels to be programmed and run on GPUs within a primarily open-source ecosystem. It has historically focused on Linux, where it offers the best support and maturity.

On the desk, the same ROCm base that you use with a Radeon for local development It is also compatible with AMD Instinct accelerators in data centers (CDNA architecture). This continuity makes it easy to develop on your team and then deploy at scale without redoing the work, leveraging the same set of libraries and tools.

For AI, this translates to frameworks like PyTorch or TensorFlow running on AMD GPUs, as well as Key tools for inference and trainingThe goal is clear: to offer an open and scalable path for research, engineering, and production workloads without proprietary lock-ins.

Hardware and platform compatibility

ROCm version 7.0.2 stands out for its expanded base of compatible devices. Supports the Radeon 9000 series (RDNA 4) and select 7000 series models (RDNA 3). It also introduces initial support for Ryzen APUs, opening the door to AI workflows on compact and portable thanks to shared memory (up to 128 GB in certain scenarios).

In terms of OS, maintains solid support on Linux, with specific mention of Ubuntu and Red Hat Enterprise Linux 9.6. On Windows, ROCm is in "Preview" phase for PyTorch, both on Radeon GPUs and on certain Ryzen APUs, which allows you to start developing natively, although with the precautions inherent to a preview.

An important detail: not all Radeon GPUs are officially supported in all versions. There are users who point out that models such as the 7800 XT do not appear as officially compatible. in certain releases of ROCm for Linux. Therefore, before installing, it is advisable to review the AMD compatibility matrices in the official documentation and verify both the ROCm and system versions, and, when necessary, export logs from GPU-Z.

In terms of capacity, the desktop Radeons can have up to 48 GB of VRAM, which makes a local workstation a powerful and private alternative to the cloudFor those who move between local development and data center deployment, cross-compatibility with Instinct simplifies migration.

Installation methods in Linux

AMD documents several approaches to installing ROCm on Linux, designed for different usage profiles and environment restrictions. It is best if you are new to ROCm to start with the guide quick start official.

Native package manager. This is the preferred method on Ubuntu or RHEL: You use the system manager itself to install, update or uninstall The packages. The advantage is that they integrate better with the system and you inherit the ecosystem support of your distribution. If your distro offers suitable repositories, this method is convenient and reproducible.

Installing one or multiple versions. When you need to test compatibility, maintain old projects, or check for regressions, You can install multiple versions of ROCm in parallelAMD publishes a specific procedure for these multi-version scenarios to isolate dependencies and avoid conflicts.

ROCm Offline Installer Creator. If your target machine doesn't have internet access or the environment is isolated, You can create an offline installation package for the AMDGPU driver, for ROCm, or for both. This utility generates everything needed for a disconnected deployment.

ROCm Runfile Installer. As an alternative to the package manager, There is a "runfile" installer It allows you to install with or without network connectivity and without depending on the distro's package system. This is useful in controlled environments or when you need a very specific version.

Note for SUSE/SLES. Before installing on SUSE Linux Enterprise Server, Register and update your Enterprise Linux according to the distribution's own procedure. This is a prerequisite to avoid dependency errors and obtain the necessary repos.

Practical installation on Ubuntu and derivatives

In recent Ubuntu environments, there are installation flows that start from repositories maintained by AMD employees (not official as such). The idea is to add the appropriate repository to your version and then install the required packages. This approach can speed up AI-oriented testing and setups.

Ubuntu versions. If you're running Ubuntu 24.04 "Noble" or 22.04 "Jammy," adjust the repository references to your release. Changing "noble" to "jammy" (or vice versa) in the repo download line is enough to align packages with your specific version.

Packages to install. Here's a special feature: there is no single metapackage "pattern" This path pulls everything necessary, so in some procedures, components are installed separately. Additionally, this path usually includes useful compilation dependencies for libraries like FlashAttention.

Python and tools. It's recommended to have Python between 3.10 and 3.13 and Git. Install ROCm, the SDK, and Python in the order that is most convenient for you. Depending on your distribution, check that PIP and virtualenv are ready to create isolated environments. This will allow you to compile or install the correct PyTorch or TensorFlow bindings for ROCm.

Other distros. This procedure has been tested primarily on Ubuntu, but some people extend it to openSUSE Leap and Slowroll adapting repos and package names. In these cases, validate the repos thoroughly, as these scenarios haven't been officially tested to the same depth.

ROCm with SD.Next: flags, Docker and fine-tuning

If your goal is to use Stable Diffusion Next (SD.Next) with AMD GPUs, the flow is straightforward: First install the ROCm libraries and launch SD.Next with the –use-rocm flag.This will force the installation of the correct version of Torch for your ROCm environment.

start Slow initial performance. On first use, after changing resolution for the first time, or when updating PyTorch, ROCm performs a search for optimal kernels which can take between 5 and 8 minutes. It happens once per resolution; subsequent runs start much faster.

MIOPEN control. If that initial warm-up is a nuisance, you can adjust environment variables: MIOPEN_FIND_MODE=FAST reduced There initially at the cost of slightly lower performance, while MIOPEN_FIND_ENFORCE=SEARCH It prioritizes optimal performance (but penalizes the initial startup more). Use them wisely according to your workflow.

Accuracy and VRAM. On RDNA 3 and higher cards, SD.Next can automatically detect bf16In some cases, this triggers a surge in VRAM usage (16 GB or more) when decoding the final image or upscaling with non-latency upscalers. To mitigate this, set the accuracy in FP16 and disables VAE upcasting in the settings. Many users also notice a performance improvement by forcing fp16.

Flash Attention on RDNA 3. To squeeze performance out of cross-attention, you can enable CK Flash Attention in Compute Settings > Cross Attention > SDP Options. It requires having rocm-hip-sdk installed because it will download and compile an additional package at boot.

Docker: yes or no? You have the option to use pre-built images To speed up deployment, or build your own image with the exact versions you need. If you prefer full control over dependencies, the DIY approach with Docker and a pinned requirements.txt file is a good practice.

Compatible ecosystem and frameworks

The latest ROCm releases place a strong emphasis on practical AI. PyTorch and TensorFlow have established support for both training and inference. on Radeon on Linux. This combination covers most current research and deployment workflows.

For large and serving models, vLLM has full supportThis facilitates efficient LLM inference on AMD GPUs. If you're using JAX, current support is focused on inference, so plan carefully if your pipeline relies on XLA training.

In the "C++ first" world, llama.cpp runs on ROCm For fast, memory-contained inference, useful when you want portability and fine-tuning of resource consumption. It's an excellent alternative for edge environments or resource-constrained systems.

ONNX Runtime with MIGraphX ​​expands deployment scope, with extended support for INT8 and INT4 in inference. This helps reduce VRAM consumption and speed up processing times when dealing with quantized models, without sacrificing acceptable production accuracy.

Finally, in the efficient training part, FlashAttention-2 enables backward pass, which improves performance and reduces memory usage in Transformers, a plus if you train or fine-tune large models locally.

Status in Windows: Preview and Alternatives

For the first time, PyTorch It has official support on Windows in "Preview" mode on Radeon GPUs and Ryzen APUs. This is positive news for those who can't switch to Linux, but it comes with the understanding that there are still areas under construction and that performance may improve with each release.

If you're looking for alternatives, there is unofficial support such as ZLUDA, which some people use to run certain workloads on Windows with AMD hardware. Paths such as DirectML, ONNX or Olive for model acceleration and compilation in the Microsoft ecosystem, with the nuances and limitations specific to each tool.

It is worth emphasizing that, since it is in preview, PyTorch on Windows over ROCm may not cover all cases nor offer the same level of stability as Linux. If your project is mission-critical, consider dual-boot environments or containers on Linux, where the stack is more mature.

Performance and troubleshooting: the good, the bad, and what you should check

There are very disparate user experiences. On the one hand, a Clear improvement in compatibility and performance With each release, especially in applied AI (PyTorch, TensorFlow, vLLM), there are also reports describing frustrating installations with dependency errors or packages that don't fit the system.

A real-world example involves a user who tested Six Ubuntu distributions in metal and, in all of them, amdgpu_install returned missing or incompatible packages. I compared the experience with Nvidia (where the installation of drivers found it "a piece of cake") and criticized the mismatch between marketing and reality when its 7800 XT was not listed as officially supported by ROCm on Linux.

In situations like this, the first step is to review the hardware compatibility matrix and versions AMD's lack of official support for a specific GPU often explains installation failures or crashes. The second step is to align the distribution, kernel, and ROCm version documented by AMD as "good" for that hardware.

If you still get stuck, consider alternative methods: installation with runfile, offline installer creator or, in specific cases, repositories maintained by AMD employees. Also keep the multi-version option handy to try an older or newer release without compromising your main environment.

For performance, remember the settings of MIOPEN_FIND_MODE and MIOPEN_FIND_ENFORCE, check the accuracy (fp16 is usually a safe bet on RDNA 3+ to balance VRAM and speed), and enable CK Flash Attention when applicable. These small changes make a noticeable difference in inference times and power consumption.

Community and useful resources

Community plays a significant role in everyday life. If you work with creative flows, the unofficial ComfyUI subreddit It's a good meeting place to share tips, tricks, and workflows. They ask you to keep your posts SFW, avoid paid workflows, stick to the topic, and, above all, be kind to those just starting out.

In addition, it is easy for you to find scripts and configurations for automate ROCm installations, setting up environments with compatible PyTorch, or tuning SD.Next. Always cross-reference what you read with the official documentation and current support matrices to avoid wasting time.

If you're just starting out, AMD's recommendation is clear: use the quick start guide And from there, scale to advanced methods (multi-version, runfile, offline) once you're clear on what specific problem they solve for you. You'll avoid unnecessary backtracking.

Overview. Let's focus on the key pieces: ROCm on Linux is currently the most stable route. for Radeon GPUs; Windows is in the build phase with PyTorch in "Preview"; hardware compatibility matters a lot; and there are proven tools (vLLM, ONNX Runtime with MIGraphX, llama.cpp, FlashAttention-2) that give muscle to real AI and computing workflows.

Anyone who wants a private local station for IA has a route with Radeon with up to 48GB of VRAMThose looking for laptops or compact PCs can explore Ryzen APUs with shared memory. Meanwhile, Docker and alternative installers offer solutions for controlled or offline environments.

Without promising miracles, with the right parts fitted and the right version for your hardware, ROCm allows you to build a serious and productive environment for AI and HPC, both in development and deployment. And if something doesn't work the first time, you're not alone: ​​the community and documentation are there to help you fine-tune it.