When a machine learning model no longer fits on a single GPU, or when a task is too large for sequential processing, developers begin to think about distributed computing. Simply put, it's about making multiple machines or chips work together as a single system.
AMD recently published a detailed guide on how to do just that using Ray and the new ROCm 7, the company's framework for GPU computing on its accelerators. Let's delve into what's happening here and why it's interesting.
What is Ray
What is Ray?
Ray is an open-source tool that allows you to run Python code across multiple machines simultaneously, as if it were a single large program. It has long been used in the ML community for conveniently distributing model training, processing data in parallel, or building complex pipelines where multiple components operate independently.
Previously, running Ray on AMD hardware required extra setup effort. Now, with the release of ROCm 7, the situation has significantly improved. Support is tighter, which means less “hoop-jumping” during deployment.
What AMD Demonstrated with Ray and ROCm 7
What Exactly Did AMD Demonstrate?
AMD's publication is more than just a compatibility announcement. It's a collection of practical scenarios with code examples, showing what you can actually accomplish with Ray on ROCm 7. These scenarios cover several levels of complexity, from relatively simple tasks to multi-component systems.
Fine-Tuning Large Language Models
One of the key scenarios is fine-tuning large language models using RLHF (Reinforcement Learning from Human Feedback). This is a method where a model is trained not just on text but on human evaluations, making its responses more helpful and accurate. This approach is used, for example, in creating chatbots.
The challenge is that RLHF is a resource-intensive process. It involves several components at once: the main model, a critic model, a generator model, and others. Keeping all of this on a single GPU is impossible. Ray allows the load to be distributed across multiple accelerators – and this is precisely what AMD demonstrates on its hardware.
Batch Processing and Parallel Inference
The second scenario is large-scale text generation. Imagine you need to run thousands of prompts through a language model – for instance, to classify documents, generate product descriptions, or label a dataset. Doing this sequentially is slow. Ray allows you to break the task into parts and process them in parallel across multiple GPUs.
AMD shows how this works in tandem with vLLM, an engine for efficient inference (i.e., running a pre-trained model to get responses). The result: the same work gets done faster, and the GPUs are loaded evenly.
Multi-Model Agent Systems
Perhaps the most interesting scenario is multi-agent systems. In short, this is when several AI models work together, each performing its own role, ultimately allowing the system to solve tasks that would be impossible for a single model.
For example, one model might be responsible for text analysis, another for information retrieval, and a third for generating the final response to the user. In this context, Ray acts as a “dispatcher”: it distributes tasks among the agents, monitors their state, and passes data between them.
AMD demonstrates a similar setup using the LangGraph framework, a tool for building agentic pipelines. In practice, this looks like a graph where the nodes are individual steps or components, and the edges represent data transfer between them. Ray handles the entire “infrastructure” side of things: who computes what, on which GPU, and in what order.
Why ROCm 7 is an Important Step for ML
Why ROCm 7 Is an Important Step
AMD has long been developing ROCm as an alternative to CUDA, NVIDIA's proprietary platform that has become the de facto standard for GPU computing in machine learning. The problem has been that most tools in the ML ecosystem were initially written for CUDA, and porting them to AMD hardware often involved a major headache.
ROCm 7 is an attempt to close this gap. AMD's publication essentially says, “Look, here are working examples with popular tools, and it all runs on our hardware without major limitations.” This is important not only for those already using AMD GPUs but also for anyone considering them as an alternative to NVIDIA.
Who Can Benefit From This Technology
Who Might Find This Useful?
First and foremost, teams that work with large models and face computational constraints. If a task “doesn't fit” on a single GPU or machine, Ray is one of the most sensible ways to scale horizontally.
It's also relevant for those building complex ML systems with multiple components, such as agents, multiple models, or parallel pipelines. Ray provides a convenient abstraction over complex infrastructure – you don't have to manually manage what runs where.
And, of course, for those who are eyeing AMD accelerators as an alternative to NVIDIA, this material is a positive signal that the ecosystem is maturing and popular tools are working as expected.
What to Consider Beyond AMD's Demonstrations
What's Left Out of the Picture?
AMD's publication is essentially a technical guide with an emphasis on the fact that everything works. This is useful, but it's worth keeping a few things in mind.
First, real-world performance in production environments may differ from the demonstration examples – this is true for any technical review. Second, the ecosystem around ROCm still lags behind CUDA in terms of maturity; some libraries and tools support AMD hardware with a delay or not at all. Third, the scenarios themselves are selected to showcase strengths – which is normal for a vendor's materials but requires a critical eye when applying them to your own tasks.
Nevertheless, the direction is clear: AMD is consistently working to make its GPUs not just physically available but also genuinely convenient to use for modern ML tasks. Ray with ROCm 7 support is one step in that direction.