Posts by Vicky Tsang
Exploring Use Cases for Scalable AI: Implementing Ray with ROCm 7 Support for Efficient ML Workflows
- 27 February 2026
This blog builds on insights from our previous blog post, which introduced Ray 2.48.0.post0 running on ROCm 6.2 and demonstrated Reinforcement Learning from Human Feedback (RLHF) with verl 0.3.0.post0 and vLLM 0.6.4 on AMD GPUs. In this follow‑up, we introduce Ray 2.51.1 with ROCm 7.0.0, verl 0.6.0, and vLLM 0.11.0.dev, highlighting the new performance benefits and capabilities for large‑scale RLHF workloads.
Elevate Your LLM Inference: Autoscaling with Ray, ROCm 7.0.0, and SkyPilot
- 13 February 2026
This blog explores autoscaling of inference workloads in Ray Serve with a vLLM backend on AMD Instinct™ GPUs for large language models (LLMs). Furthermore, you will learn how to scale beyond a single cluster using SkyPilot, which enables multicloud scaling for Ray Serve. Combined with the AMD ROCm™ software platform, this creates a unified, cloud-agnostic platform that scales distributed LLM inference from single-GPU to multi-cluster deployments.
Reinforcement Learning from Human Feedback on AMD GPUs with verl and ROCm 7.0.0
- 12 February 2026
In our previous blog post, we introduced Volcano Engine Reinforcement Learning for LLMs (verl) 0.3.0.post0 with ROCm 6.2 and vLLM 0.6.4. In this blog post, we will provide you with an overview of verl 0.6.0 with ROCm 7.0.0 and vLLM 0.11.0.dev and its benefits for large-scale reinforcement learning from human feedback (RLHF). You will also learn about the modifications made to optimize verl performance on AMD Instinct™ MI300X GPUs. Next, you will walk through building the Docker image on your system, along with training scripts for single-node and multi-node setups. Lastly, we provide you with verl performance results, focusing on throughput and convergence accuracy achieved on AMD Instinct MI300X GPUs. Follow this guide to get started with verl on AMD Instinct GPUs and accelerate your RLHF training with ROCm-optimized performance.
Exploring Use Cases for Scalable AI: Implementing Ray with ROCm Support for Efficient ML Workflows
- 10 September 2025
In this blog, you will learn how to use Ray to easily scale your AI applications from your laptop to multiple AMD GPUs.
Reinforcement Learning from Human Feedback on AMD GPUs with verl and ROCm Integration
- 24 April 2025
In this blog post, we provide an overview of Volcano Engine Reinforcement Learning for LLMs (verl) and discuss its benefits in large-scale reinforcement learning from human feedback (RLHF). We also detail the modifications made to the codebase to optimize verl’s performance on AMD Instinct GPUs. Next, we walk through the process of building the Docker image using a Dockerfile on the user side, along with training scripts tailored for both single-node and multi-node setups. Lastly, we present verl’s performance results, focusing on throughput and convergence accuracy achieved on AMD Instinct™ MI300X GPUs. Follow this guide to get started with verl on AMD Instinct GPUs and accelerate your RLHF training with ROCm-optimized performance.