Featured Posts
ROCm 7.9 Technology Preview: ROCm Core SDK and TheRock Build System
Introduce ROCm Core SDK, and learn to install and build ROCm components easily using TheRock.
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
Reproducing the AMD Instinct™ GPUs MLPerf Inference v5.1 Submission
In this blog, we will provide step by step instruction on how to reproduce AMD's MLPerf Inference v5.1 Submission
Performance Profiling on AMD GPUs - Part 3: Advanced Usage
Part 3 of our GPU profiling series guides beginners through practical steps to identify and optimize kernel bottlenecks using ROCm tools
Empowering Developers to Build a Robust PyTorch Ecosystem on AMD ROCm™ with Better Insights and Monitoring
At AMD, the PyTorch ecosystem team is committed to delivering an exceptional out-of-the-box experience for developers. Over the past year, the team has made significant progress in expanding PyTorch ecosystem support, improving CI test coverage across a wider range of GPU architectures, enhancing training and inference capabilities, streamlining the developer experience, introducing new functionality and performance optimizations, and strengthening quality monitoring. This blog showcases our ongoing efforts to build a robust PyTorch ecosystem on AMD ROCm™ Software, including the production-readiness of PyTorch across N-1, N, and N+1 releases aligned with ROCm versions. We also introduce the AI SoftWare Heads-Up Dashboard (AISWHUD), a powerful new tool that provides deep insights into the health and performance of the PyTorch ecosystem on ROCm, empowering developers with greater visibility and control.
Kimi-K2-Instruct: Enhanced Out-of-the-Box Performance on AMD Instinct MI355 Series GPUs
Learn how AMD Instinct MI355 Series GPUs deliver competitive Kimi-K2 inference with faster TTFT, lower latency, and strong throughput.
Gumiho: A New Paradigm for Speculative Decoding — Earlier Tokens in a Draft Sequence Matter More
Gumiho boosts LLM inference with early-token accuracy, blending serial + parallel decoding for speed, accuracy, and ROCm-optimized deployment.
Llama.cpp Meets Instinct: A New Era of Open-Source AI Acceleration
performance optimizations for llama.cpp on AMD Instinct GPUs
Day 0 Developer Guide: Running the Latest Open Models from OpenAI on AMD AI Hardware
Day 0 support across our AI hardware ecosystem from our flagship AMD InstinctTM MI355X and MI300X GPUs, AMD Radeon™ AI PRO R700 GPUs and AMD Ryzen™ AI Processors
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.
AMD ROCm: Powering the World's Fastest Supercomputers
Discover how ROCm drives the world’s top supercomputers, from El Capitan to Frontier, and why its shaping the future of scalable, open and sustainable HPC
Announcing MONAI 1.0.0 for AMD ROCm: Breakthrough AI Acceleration for Medical Imaging Models on AMD Instinct™ GPUs
Learn how to use Medical Open Network for Artificial Intelligence (MONAI) 1.0 on ROCm, with examples and demonstrations.
Medical Imaging on MI300X: Optimized SwinUNETR for Tumor Detection
Learn how to setup, run and optimize SwinUNETR on AMD MI300X GPUs for fast medical imaging 3D segmentation of tumors using fast, large ROIs.
Optimizing FP4 Mixed-Precision Inference with Petit on AMD Instinct MI250 and MI300 GPUs: A Developer’s Perspective
Learn how FP4 mixed-precision on AMD GPUs boosts inference speed and integrates seamlessly with SGLang.
Optimizing Drug Discovery Tools on AMD MI300s Part 2: 3D Molecular Generation with SemlaFlow
Learn how to set up, run, and optimize SemlaFlow, a molecular generation tool, on AMD MI300X GPUs for faster drug discovery workflows
GEMM Tuning within hipBLASLt– Part 2
Learn how to use hipblaslt-bench for offline GEMM tuning in hipBLASLt—benchmark, save, and apply custom-tuned kernels at runtime.
Elevating 3D Scene Rendering with GSplat
ROCm Port of GSplat - GPU accelerated rasterization of Gaussian splatting
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
An Introduction to Primus-Turbo: A Library for Accelerating Transformer Models on AMD GPUs
Primus streamlines training on AMD ROCm, from fine-tuning to massive pretraining on MI300X GPUs—faster, safer, and easier to debug
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