Exploring Use Cases for Scalable AI: Implementing Ray with ROCm 7 Support for Efficient ML Workflows

2026-02-27 · Source: AMD ROCm Blogs · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Intermediate, extended

Summary

This blog post details the implementation and use cases of Ray 2.51.1 with ROCm 7.0.0, verl 0.6.0, and vLLM 0.11.0.dev for efficient, scalable AI/ML workflows on AMD GPUs. It builds upon previous work with Ray 2.48.0.post0 and ROCm 6.2, focusing on enhanced performance for large-scale Reinforcement Learning from Human Feedback (RLHF) workloads. The content provides hands-on, example-driven workflows covering distributed RLHF training with verl, autoscaling inference with SkyPilot, Ray Serve applications, vLLM-backed inference, hyperparameter tuning with Ray Tune, Stable Diffusion image generation, and multi-GPU fine-tuning with Ray Train. Key performance metrics show AMD Instinct MI300X 8x GPU offering up to 56% higher PPO training throughput and 12% higher GRPO training throughput compared to NVIDIA H100 on specific LLM models.

Key takeaway

For MLOps Engineers and Deep Learning Engineers building scalable AI applications on AMD hardware, integrating Ray with ROCm 7.0.0 offers significant performance advantages. You should explore Ray's ecosystem (Tune, Serve, Train) to streamline distributed training, inference, and serving, especially for large language models and generative AI. This combination can lead to substantial throughput improvements, as demonstrated by the AMD Instinct MI300X 8x GPU's performance gains over NVIDIA H100.

Key insights

Ray and ROCm 7.0.0 enable scalable, efficient AI/ML workflows on AMD GPUs, particularly for LLMs.

Principles

• Abstract distributed complexity with Ray primitives.
• Optimize ML workloads with ROCm acceleration.
• Scale training and inference across multiple GPUs/nodes.

Method

Install Ray with ROCm support via Docker, then deploy and scale various ML applications (RLHF, LLM inference, hyperparameter tuning, image generation, transformer fine-tuning) using Ray's ecosystem libraries.

In practice

• Use `vllm serve` for distributed LLM inference.
• Employ `Ray Tune` for efficient hyperparameter optimization.
• Scale transformer fine-tuning with `Ray Train` and `ScalingConfig`.

Topics

• Ray Framework
• ROCm
• Distributed Machine Learning
• Large Language Models
• Reinforcement Learning from Human Feedback

Code references

• ray-project/ray
• ROCm/ray

Best for: Machine Learning Engineer, Deep Learning Engineer, MLOps Engineer

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• ROCm 7.13: Expanding Hardware, Tools, and Reach — ROCm 7.13 expands AMD GPU hardware support, virtualization, and developer tools for AI workloads.
• Maximizing GPU Utilization: Heterogeneous Pipelines with Ray and Kubernetes — Ray optimizes heterogeneous, distributed AI workloads by managing diverse compute resources and handling failures across complex, multi-layered stacks.
• Ray Online Courses for Distributed AI & ML* — Ray is an open-source distributed computing framework for scaling AI/ML workloads across heterogeneous clusters.
• Introducing AI Runtime: Scalable, Serverless NVIDIA GPUs on Databricks for Training and Finetuning — AI Runtime simplifies distributed GPU training by offering serverless, on-demand access and optimized tools within Databricks.
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Editorial summary, takeaway, and curation by AIssential. Original article published by AMD ROCm Blogs.