Provide instructions to potential customers and partners to verify our MLPerf Inference v6.0 submission result.

A showcase of how to run NeuralGCM, a hybrid GCM model, on AMD Instinct hardware, including an introduction, installation, inference, and plotting.

Learn how to build high-performance FP8 GEMM kernels on AMD CDNA™4 GPUs using MFMA, LDS swizzling, and double-buffering.

we highlight the latest ROCm 7.2 enhancements for AMD Instinct GPUs, designed to boost AI and HPC performance

Learn how to semantically split vision datasets using foundation model embeddings on AMD GPUs to reduce leakage and improve evaluation.

Quantize Kimi-K2.5 to W4A8 and W8A8 using AMD Quark and serve on MI325X with FlyDSL and AITER for further inference acceleration.

We show that the MI355X delivers better performance than the B200 for ComfyUI after enabling PyTorch attention for gfx950.

Use ATOM as an out-of-tree vLLM plugin to keep vLLM compatibility while enabling AMD-optimized attention, model execution, and multi-model support including Kimi-K2.5.

Hands-on, reproducible guide to train and run Eruku on LUMI supercomputer, powered by AMD Instinct MI250X GPUs.

Learn how to use multi-node and multi-cluster autoscaling in the Ray framework on ROCm 7.0.0 with SkyPilot

This blog post gives a walkthrough of how to deploy a robotics application on the AI PC integrated with ROS - the robot operating system. We showcase Ryzen AI CVML Library to do perception tasks like depth estimation and develop a custom ROS 2 node which allows easy integration with the ROS ecosystem and standard components.

Learn how to leverage TileLang to develop your own kernel. Explore the power to fully utilize AMD GPUs

Run Street Gaussians on AMD Instinct MI300: migrate to latest gsplat, install on ROCm, and render dynamic street scenes.

Explore a new MoE architecture designed for native computation-communication overlap, enabling efficient distributed execution.

This blog explores a new training-free loosely speculative decoding method, that can accept mismatches that are semantically valid and speedup original SPD method.

Learn how to deploy AI models on AMD GPUs with Triton Inference Server, now supporting ONNX Runtime and Python backends, and see performance benchmarks.

Explore how TraceLens automates profiler trace analysis to pinpoint bottlenecks and optimize AI workloads.

Learn how to use the Primus projection tool to estimate memory and performance for large-scale LLM training on AMD Instinct™ accelerator platforms.

A practical guide to installing and using FlyDSL nightly wheels on ROCm for fast, Python-native GPU kernel development

Profiling tools