AMD ROCm is changing the way we use graphics cards. They’re no longer just a tool for gaming – today they’re at the core of artificial intelligence, 3D graphics, scientific simulations and massive data processing. For many years, Nvidia dominated this world with its CUDA platform, but an increasingly powerful open source alternative from AMD is coming onto the scene – namely ROCm.
In this article, we’ll explain what AMD ROCm is, why it was created, how it works, what benefits it brings, and why you should care – whether you’re a beginner or a professional.
What is AMD ROCm ?
AMD ROCm (Radeon Open Compute Platform) is an open compute platform that transforms Radeon graphics cards into a tool for artificial intelligence, scientific computing, 3D graphics, and big data processing. In other words, with ROCm, the graphics card can do what the processor alone couldn’t do fast enough.
It’s not just one program, but an entire ecosystem – from graphics card drivers, to developer libraries, to tools that allow you to port applications from other platforms. As a result, ROCm can be used equally well on a home computer or on supercomputers, which are among the most powerful in the world.
For a better understanding:
- The CPU (processor) is the universal “brain” of the computer. It can process everything, but it does so sequentially, and therefore quickly becomes a bottleneck with huge amounts of data.
- The GPU (graphics card) has hundreds to thousands of smaller computing cores. These work simultaneously and can handle large volumes of data at once.
Think of the CPU as a single chef who cooks an entire menu by himself. The GPU is a team of hundreds of cooks preparing meals together. The result is finished significantly faster.
This makes GPUs ideal for tasks like:
- aI training,
- numerical simulations,
- 3D rendering,
- scientific computing,
- big data processing.
AMD ROCm acts as a bridge between the software and the graphics card. It ensures that programs can harness this parallel power efficiently and turn the raw power of the GPU into practical results.
How does AMD ROCm work ?
AMD ROCm is a multi-layered system where each part performs its role and together they ensure that the graphics card can process calculations quickly and efficiently.
The way it works is that software (such as an artificial intelligence application or a 3D graphics program) sends a request for a computation. This request is passed through the various ROCm layers, which convert it into a form that the GPU understands and make sure that the task is executed in parallel on hundreds or thousands of compute cores.
The main building blocks that provide this process are:
- ROCK – a graphics card driver that allows the operating system to communicate with the GPU at a low level.
- ROCr runtime – the environment that translates commands from software into instructions for the graphics card and manages its operation.
- HIP (Heterogeneous Interface for Portability) – an interface that allows code to be written to be portable between different platforms and manufacturers.
- HIPIFY – a tool that can automatically convert programs written for CUDA (Nvidia) to HIP format so that they also work on AMD cards.
- Developer libraries – ready-made packages of functions that speed up specific types of computations. For example, MIOpen for neural networks or libraries for mathematical operations such as BLAS and FFT.
In practice, this means that the user launches an application, the application sends a request via ROCm, ROCm translates it and the graphics card performs the computation. The whole process is automatic – the user only sees the result in the form of a faster render, a trained AI model or processed data.
ROCm thus acts as a bridge between software and hardware: programs get a simple environment to work in, and the graphics card takes care of the performance.
Why was AMD ROCm created ?
For many years, the world of graphics card computing was practically dependent on Nvidia and its CUDA system. It only ran on Nvidia cards, so most AI or scientific computing applications only ran on one type of hardware.
AMD decided to break this dependency and created ROCm – an open platform that:
- makes the power of Radeon graphics available outside of games,
- allows developers to port code between Linux and Windows,
- brings more competition and choice for users.
Today, ROCm is no longer just the stuff of supercomputers and datacenters – it’s becoming accessible to everyday people.
HIP SDK – the big breakthrough
HIP SDK (Heterogeneous Interface for Portability Software Development Kit) is a crank tool from AMD for developers. Its main purpose is to enable programs written for Nvidia (CUDA) to run on AMD graphics cards.
Before the HIP SDK, the problem was clear – most AI and scientific computing applications were written for CUDA. If someone wanted to use AMD graphics, they had to rewrite the code practically from scratch. This was tedious, expensive, and for many, demotivating.
The HIP SDK removes this barrier:
- it acts as a compatibility layer between CUDA and AMD,
- it can automatically “translate” CUDA code into HIP format,
- in most cases, only a minor modification to the program is needed.
In practice, this means that a program that used to run only on Nvidia can now also run on Radeon graphics. This makes life easier for developers, saves time and opens up new possibilities for users – they can choose hardware based on price or availability, rather than who the program was originally written for.
Where ROCm works and what it offers
In the beginning, AMD ROCm was designed for Linux only. This meant that it could be used mainly by researchers and professionals in data centres. Today, it also runs on Windows, so it has become available to a wider audience – from professionals to regular home users.
Similarly, graphics card support has also expanded. At first, it worked only on professional models like AMD Instinct or Radeon Pro. Gradually, however, support was added for classic gaming and consumer cards as well – for example, the Radeon RX 7000 and RX 9000 series. What’s more, AMD has promised that all new graphics cards will have ROCm support right from launch. This means users no longer have to wait months for the necessary drivers or libraries.
Ecosystem of tools and applications
ROCm is designed to fit well into existing workflows. It supports the most popular libraries and frameworks used in AI and computing, including PyTorch, TensorFlow, ONNX, MXNet, CuPy, Caffe, and llama.cpp.
It also plays a significant role in creative environments. As of version 3.0, Blender can use AMD ROCm (via HIP technology) for rendering. In practice, this means that 3D graphics and animation creators can use the full power of their Radeon cards without limitations.
However, ROCm is not limited to home computers. It is also used in the world’s most powerful supercomputers, such as Frontier and the upcoming El Capitan, which are pushing the boundaries of science and technology.
Performance and optimization
One of AMD ROCm’s biggest strengths is the ability to tailor performance to a specific graphics card. This process is called auto-tuning – the system can automatically find the best way to run calculations on a given card.
According to a 2024 study, auto-tuning on AMD GPUs delivered up to a tenfold speedup. By comparison, on Nvidia cards it was only double. This shows that AMD’s architecture has a lot of potential, especially when software can adapt to specific hardware.
AMD ROCm summary
AMD ROCm is an open platform that transforms Radeon graphics cards into a tool for artificial intelligence, simulation, 3D graphics, and big data work. It has long been designed primarily for supercomputers and data centers, but today it is becoming available to mainstream users as well.
In practice, this means:
- aI applications such as Stable Diffusion or local chatbots run faster on Radeon,
- creatives get extra power for video editing and 3D rendering,
- students and researchers get an affordable alternative to Nvidia.
At the same time, AMD is investing in the future of ROCm – simplifying installation, expanding support for systems and graphics cards, and building entire ecosystems of libraries. The goal is clear, and that is to make GPU performance available to a wider audience and offer an open alternative to Nvidia’s CUDA.
For users, this means only one thing – powerful and affordable computing power is closer than ever.