Follow this step-by-step guide to reproduce AMDs MLPerf 5.0 Training Submission with Instinct GPUs using ROCm

Explore the techniques we used to improve the training performance on MI300X and MI325X in our MLPerf Training 5.0 submission.

Learn how to optimize BERT-L training with mixed precision and Flash Attention v2 on AMD Instinct GPUs — follow our tested MLPerf-compliant step-by-step guide.

Learn how to horizontally scale LLM inference using open-source tools on MI300X, with vLLM, nginx, Prometheus, and Grafana.

Learn how to use Meta’s Llama Stack with AMD ROCm and vLLM to scale inference, integrate APIs, and streamline production-ready AI workflows on AMD Instinct™ GPU

Explore ROCm 6.4's key advancements: AI/HPC performance boosts, enhanced profiling tools, better Kubernetes support and modular drivers, accelerating AI and HPC workloads on AMD GPUs.

We announce AMD’s support of Open Platform for Enterprise AI (OPEA), integrating OPEA’s enterprise GenAI framework with AMD’s computing hardware and ROCm software

This blog shows Zyphra's new training kernels for transformers and hybrid models on AMD Instinct MI300X accelerators, surpassing the H100s performance

AMD Integrates llm-d on AMD Instinct MI300X Cluster For Distributed LLM Serving

Learn how to accelerate text-to-video generation using Step-Video-T2V, a 30B parameter T2V model, on AMD MI300X GPUs with ROCm—enabling scalable, high-fidelity video generation from text

This blog post demonstrates how hipDF significantly enhances and accelerates data manipulation, aggregation, and transformation tasks on AMD hardware using ROCm.

Learn how to deploy CuPy and hipDF on AMD GPUs. See their high-performance computing advantages, and use CuPy and hipDF in a detailed example of an investment portfolio allocation optimization using the Markowitz model.

Learn how to implement FlashAttention-v2 with CK-Tile: minimize memory overhead, maximize compute efficiency, and scale on AMD GPUs

Learn how to boost your Llama 4 inference performance on AMD MI300X GPUs using AITER-optimized kernels and advanced vLLM techniques

This blog shows you how to speedup your multimodal models with AMD’s open-source PyTorch tools for speculative decoding on MI300X GPUs

Build high-performance GEMM kernels using CK-Tile on AMD Instinct GPUs with vendor-optimized pipelines and policies for AI and HPC workloads