Systems Blogs#
GPU Partitioning Made Easy: Pack More AI Workloads Using AMD GPU Operator
What’s New in AMD GPU Operator: Learn About GPU Partitioning and New Kubernetes Features
Matrix Core Programming on AMD CDNA™3 and CDNA™4 architecture
This blog post explains how to use Matrix Cores on CDNA3 and CDNA4 architecture, with a focus on low-precision data types such as FP16, FP8, and FP4
ROCm 7.0: An AI-Ready Powerhouse for Performance, Efficiency, and Productivity
Discover how ROCm 7.0 integrates AI across every layer, combining hardware enablement, frameworks, model support, and a suite of optimized tools
Unlocking GPU-Accelerated Containers with the AMD Container Toolkit
Simplify GPU acceleration in containers with the AMD Container Toolkit—streamlined setup, runtime hooks, and full ROCm integration.
ROCm Revisited: Getting Started with HIP
New to HIP? This blog will introduce you to the HIP runtime API, its key concepts and installation and practical code examples to showcase its functionality.
ROCm Gets Modular: Meet the Instinct Datacenter GPU Driver
We introduce the new Instinct driver-a modular GPU driver with independent releases simplifying workflows, system setup, and enhancing compatibility across toolkit versions.
Stone Ridge Expands Reservoir Simulation Options with AMD Instinct™ Accelerators
Stone Ridge Technology (SRT) pioneered the use of GPUs for high performance reservoir simulation (HPC) nearly a decade ago with ECHELON, its flagship software product. ECHELON, the first of its kind, engineered from the outset to harness the full potential of massively parallel GPUs, stands apart in the industry for its power, efficiency, and accuracy. Now, ECHELON has added support for AMDInstinct accelerators into its simulation engine, offering new flexibility and optionality to its clients.
AMD Collaboration with the University of Michigan offers High Performance Open-Source Solutions to the Bioinformatics Community
We are thrilled to share the success story of a 1.5-year collaboration between AMD and the University of Michigan, Ann Arbor where we used the AMD Instinct™ GPUs and ROCm™ software stack to optimize the sequence alignment bottleneck in long read processing workflows.
Deploying Serverless AI Inference on AMD GPU Clusters
This blog helps targeted audience in setting up AI inference serverless deployment in a kubernetes cluster with AMD accelerators. Blog aims to provide a comprehensive guide for deploying and scaling AI inference workloads on serverless infrastructre.
ROCm Runfile Installer Is Here!
Overview of ROCm Runfile Installer introduced in ROCm 6.4, allowing a complete single package for driver and ROCm installation without internet connectivity
Installing ROCm from source with Spack
Install ROCm and PyTorch from source using Spack. Learn how to optimize builds, manage dependencies, and streamline your GPU software stacks.
What's New in the AMD GPU Operator v1.2.0 Release
This blog highlights the new feature enhancements that were released as part of the AMD GPU Operator v1.2.0 release. New features that enhance the use of AMD Instinct GPUs on Kubernetes including Automated Upgrades, Health Checks and Open-sourcing the codebase.
Announcing the AMD GPU Operator and Metrics Exporter
This post announces the AMD GPU Operator for Kubernetes and and the Device Metrics Exporter, including instructions for getting started with these new releases.
Stay informed
- Subscribe to our RSS feed (Requires an RSS reader available as browser plugins.)
- Signup for the ROCm newsletter
- View our blog statistics