AMD ROCm Blogs

Getting Started with ComfyUI on AMD Radeon™ RX 9000 Series GPUs

2026-03-09T00:00:00+00:00

ComfyUI has become a widely adopted and versatile node-based interface for Stable Diffusion and other generative AI models, gaining significant traction within the AI content creation community. Unlike traditional web-based interfaces, ComfyUI provides a node-based workflow system that gives users complete control over their image and video generation pipelines. Its modular architecture allows for complex workflows involving multiple models, LoRAs, ControlNets, and custom processing steps.

Agentic Diagnosis for LLM Training at Scale

2026-03-09T00:00:00+00:00

In MaxText-Slurm: Production-Grade LLM Training with Built-In Observability, we introduced MaxText-Slurm — an open-source launch system and observability stack for running MaxText LLM training on AMD Instinct GPU clusters. We showed how a unified Prometheus time-series database (TSDB) collects GPU, host, network, and training metrics into a single queryable store, persisted to disk so that no data is lost even if the job crashes.

HPC Coding Agent - Part 3: MCP Tool for Profiling

2026-03-06T00:00:00+00:00

In this blog, we build an AI agent specialized in profiling and optimizing GPU-accelerated applications within High-Performance Computing (HPC) environments. Using open-source tools, we create a state-of-the-art agent and enhance its profiling capabilities through a custom Model Context Protocol (MCP) server. This server provides the agent with tools to leverage AMD’s profiling utilities for analyzing application performance on AMD GPUs.

Fine-Tuning AI Surrogate Models for Physics Simulations with Walrus on AMD Instinct GPU Accelerators

2026-03-06T00:00:00+00:00

Physics simulations are used for studying complex systems and are essential where experiments are difficult, expensive, or impossible. In our context, a simulation means numerically solving mathematical equations that are believed to describe a physical system and evolving them forward in time on a computer. They enable controlled exploration of physical behavior for science and engineering, but at a high computational cost, which in most cases increases rapidly with scale. Our focus is on continuum dynamics, where the system is represented by fields such as density, velocity, or temperature, defined on a grid and evolving over time. High-resolution physics simulations are slow to run, sensitive to numerical error and impractical for large parameter spaces. Surrogate models address these limitations by learning to approximate simulation dynamics directly from data. Once trained, they can produce fast predictions at a fraction of the cost, giving researchers the ability to rapidly explore parameter space and generate long rollouts.

Ensemble High-Resolution Weather Forecasting on AMD Instinct GPU Accelerators

2026-03-06T00:00:00+00:00

Weather prediction is fraught with uncertainty, as is the inference of any real-world phenomena dependent on physical observations. The consequence is that any estimated current state of the atmosphere as well as any forecast both carry a level of uncertainty. As such, any weather forecasting model, whether AI or traditional, needs to produce reasonable outputs despite the inherent uncertainty of inputs, and, if possible, quantify the uncertainty of the outputs for the user in some practical fashion.

HPC Coding Agent - Part 2: An MCP Tool for Code Optimization with OpenEvolve

2026-03-04T00:00:00+00:00

Large language models (LLMs) and LLM-driven agents (AI agents) are already trained on a massive amount of data where a considerable portion consists of code, and both models and agentic coding services are developed specifically for the purpose of coding. For users who want to optimize their code for certain purposes, for example runtime or memory efficiency, LLMs may produce plausible solutions, but these are often not optimal.

Streamlining Recommendation Model Training on AMD Instinct™ GPUs

2026-03-02T00:00:00+00:00

Recommendation model training and inference workloads represent a significant portion of computational requirements across industries including e-commerce, social media and content streaming platforms. Unlike LLMs, recommendation models result in to complex and often imbalanced communication across GPUs, along with a higher load on the CPU-GPU interconnect. The ROCm training docker [1] now includes essential libraries for recommendation model training. This blog demonstrates the functionality and ease of training recommendation models using ROCm, along with suggestions for improved configuration of these workloads. We also highlight the inherent benefits of the large HBM size on AMD Instinct™ GPUs for recommendation workloads.

MaxText-Slurm: Production-Grade LLM Training with Built-In Observability

2026-03-02T00:00:00+00:00

Training large language models (LLMs) at scale on GPU clusters is not just a compute problem — it is an operations problem. Launching multi-node distributed training, keeping it running reliably, and diagnosing failures when they happen all require tooling that most training frameworks do not provide. MaxText-Slurm is an open-source launch system and observability stack that bridges this gap for MaxText on AMD Instinct GPU clusters managed by Slurm.

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

2026-02-27T00:00:00+00:00

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.

PyTorch Offline Tuning with TunableOp

2026-02-24T00:00:00+00:00

In an earlier blog post, we explored how PyTorch TunableOp can potentially accelerate models through online tuning - where during model execution, PyTorch benchmarks and selects optimal BLAS kernels. While online tuning is effective, it introduces overhead due to the time needed to execute the ML model from end-to-end. If this is done once, the overhead may be acceptable, but for repeated tuning it may be cost-prohibitive to keep re-running the model.