Featured content

Tech Explainer: What’s new in AMD ROCm 7?

Learn how the AMD ROCm software stack has been updated for the era of AI.

  • November 20, 2025 | Author: KJ Jacoby
Learn More about this topic

While GPUs have become the digital engines of our increasingly AI-powered lives, controlling them accurately and efficiently can be tricky.

That’s why, in 2016, AMD created ROCm. Pronounced rock-em, it’s a software stack designed to translate the code written by programmers into sets of instructions that AMD GPUs can understand and execute perfectly.

If the GPUs in today’s cutting-edge AI servers are the orchestra, then ROCm is the sheet music being played.

AMD introduced the latest version, ROCm 7.0, earlier this fall. Version 7.0 is designed for the new world of AI.

How ROCm works

ROCm is a platform created by AMD to run programs on its AI-focused GPUs, the Instinct MI350 Series accelerators. AMD calls the latest version, ROCm 7.0, an AI-ready powerhouse designed for performance, efficiency and productivity.

Providing that kind of facility is a matter of far more than just simple software. ROCm is actually an expansive collection of tools, drivers and libraries.

What’s in the collection? The full ROCm stack contains:

  • Drivers that enable a computer’s operating system to communicate with any installed AMD GPUs.
  • The Heterogeneous Interface for Portability (HIP), a coding system for users to create and run custom GPU programs.
  • Math and AI libraries including specialized tools like deep learning operations, fast math routines, matrix multiplication, and tensor ops. These AI building blocks are pre-built to help developers accelerate production.
  • Compilers that turn code into GPU instructions.
  • System-management tools that developers can use to debug applications and optimize GPU performance.

Help Me, GPU

The latest version of ROCm is purpose-built for generative AI and large-scale AI inferencing and training. While developers rely on GPUs for parallel processing, performing many tasks at once, GPUs are general-purpose processors. To achieve the best performance for AI workloads, developers need a software bridge that turns their high-level code into GPU-optimized instructions. That bridge is ROCm.

ROCm lets developers run AI frameworks that include PyTorch effectively on AMD GPUs. ROCm converts application code into instructions designed for the hardware. In this way, ROCm helps organizations improve performance, scale workloads across multiple GPUs, and meet increasing demand without sacrificing reliability.
 
For demanding AI workloads such as those using Mixture of Experts (MoE) models, ROCm is essential for execution. MoE models activate only a small group of expert networks for each input, resulting in sparse workloads that are efficient, but hard to schedule. ROCm ensures that GPUs can perform these sparse operations at scale, maintaining high throughput and accuracy across clusters.
 
In other words, ROCm provides the tools and runtime to make even the most complex GPU workloads run efficiently. It connects AI developers with the hardware that supports their applications.
 
That’s important. While increased demand is what every enterprise wants, it still brings challenges that leave little room for mistakes.
 
Open Source Power

But wait, there's more. AMD ROCm has another clever trick up its sleeve: open-source integration.

By using popular open-source frameworks, ROCm lets enterprises and developers run large-scale inference workloads more efficiently. This open source approach also empowers the same organizations and developers to break free of proprietary software and vendor-locked ecosystems.

Free from those dependencies, users can scale AI clusters by deploying commodity components instead of being locked into a single vendor’s hardware. Ultimately, that can lead to lower hardware and licensing costs.

This approach also empowers users to customize their AI operations. In this way, AI systems can be developed to better suit the unique requirements of an organization’s applications, environments and end users.

Another Layer

While ROCm serves the larger market, the recent release of AMD’s new Enterprise AI Suite shows the company’s commitment to developing tools specifically for enterprise-class organizations.

AMD says the new suite can to take enterprises from bare metal server to enterprise-ready AI software in mere minutes.

To accomplish this, the suite provides four additional components: solution blueprints, inference microservices, AI Workbench, and a dedicated resource manager.

These tools are designed to help enterprises better scale their AI workloads, predict costs and capacity, and accelerate time-to-production.

Always Be Developing

Along with these product releases, AMD is being perfectly clear about its focus on AI development. At the company’s recent Financial Analyst Day, AMD CEO Lisa Su explained that over the last five years, the cost of AMD’s AI-related investments and acquisitions has topped $100 billion. That includes building up a staff of some 25,000 engineers.

Looking ahead, Su told financial analysts that AMD’s data-center AI business is on track to draw revenue in the “tens of billions of dollars” by 2027. She also said that over the next three to five years, AMD expects its data-center AI revenue to enjoy a compound annual growth rate (CAGR) of over 80%.

AMD’s roadmap points to updates that will focus on further boosts to performance, productivity and scalability. The company may accomplish these gains by offering more streamlined build and packaging systems, more optimized training and inferencing, and broader hardware support. It’s also reasonable to expect improved virtualization and multi-tenant support.

That said, if you want your speculation about future AI-centric ROCm improvements to be as accurate as possible, your best bet may be to ask an AI chatbot…powered by Supermicro and AMD, of course.

Do More:

 

Featured videos

Events

Find AMD & Supermicro Elsewhere