Systems Blogs#
Debugging NaN Results in CK Tile GEMM: A rocgdb Detective Story
Learn GPU kernel debugging with rocgdb through a real case: tracing NaN outputs to a one-character typo in CK Tile GEMM
ROCm 7.2: Smarter, Faster, and More Scalable for Modern AI Workloads
we highlight the latest ROCm 7.2 enhancements for AMD Instinct GPUs, designed to boost AI and HPC performance
ROCm Becomes a First-Class Platform in the vLLM Ecosystem
ROCm is now a first-class vLLM platform: official wheels + Docker, stronger CI, and faster LLM & multimodal inference on AMD Instinct GPUs.
Reimagining GPU Allocation in Kubernetes: Introducing the AMD GPU DRA Driver
Explore how the AMD GPU DRA Driver brings declarative, attribute-aware GPU scheduling to Kubernetes — learn how to request and manage GPUs natively
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.
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.
Introducing the AMD Network Operator v1.0.0: Simplifying High-Performance Networking for AMD Platforms
Introducing the AMD Network Operator for automating high-performance AI NIC networking in Kubernetes for AI and HPC workloads
Practical, Fault‑Robust Distributed Inference for DeepSeek on AMD MI300X
Learn how a small-radius expert parallel design with prefill–decode disaggregation enables scalable, fault-isolated LLM inference on AMD Instinct™ MI300X clusters.
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
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