The latest update to ROCm 6.1 introduces numerous capabilities for developers and researchers alike. The new features and fixes provided by ROCm 6.1 are designed to improve the stability and performance of applications, enabling AI and HPC developers to explore the farthest boundaries of what's possible.

AMD has unveiled the latest release of its open software, AMD ROCm™ 6.1.3, marking the next step in its strategy to make ROCm software broadly available across its GPU portfolio, including AMD Radeon™ desktop GPUs. The new release gives developers broader support for Radeon GPUs to run ROCm AI workloads.

Did you know that you can build your own coding Copilot just using the AMD Radeon graphic card locally? This blog will share with you how to build your personal coding Copilot with Radeon graphic card, Continue, and LM Studio plus Llama3.

As one of AMD's independent software vendors, GamePP developed an application based on Ryzen™ AI, utilizing the AI acceleration of the on-board AMD NPU to deliver a better gaming experience on Ryzen AI-powered laptops.

The top conference AAAI 2024 has successfully accepted a recent paper from the AMD. This blog shares about keys of an efficient depth pruning method for both CNN and Vision Transformer, and how this method helps outstanding compression performance across various AI models. 

Written by: Hisham Chowdhury (AMD), Sonbol Yazdanbakhsh (AMD), Lucas Neves (AMD)

Introducing 4-bit quantization for Language Models (LM) on AMD Ryzen™ AI and Radeon™ platforms with DirectML

It's an exciting time for Next-Gen AI PCs! Microsoft unveiled a suite of upcoming transformative AI features, including all-new CoPilot+ experiences that will fundamentally change the way we work and interact with our PCs. Users can be confident that AMD “Strix Point” systems will be Windows 11 ready for Copilot+ experiences as soon as those are ready to load in the not-too-distant future.

Visual computing has transformed how we work, experiment, share ideas, and engage the world. New technologies are changing the game for modern workforces—opening up valuable opportunities in business, science, and technology. But many businesses are settl

AMD invests significant financial resources to provide on-premises and multi-cloud EDA compute infrastructure for its engineers. At AMD IT, we continuously optimize our cloud/data center components. We have traditionally used machine learning (ML) and business intelligence (BI) tools to predict low-level capacity metrics and drive efficiencies. In this article, we will highlight the challenges in managing EDA compute forecast, our approach to the problem, and how we integrate ML/Generative AI in the compute investment optimization process in IT.

AMD is advancing AI with an open ecosystem through its open-source software ROCm^TM, which is designed for GPUs, with a collection of drivers, software tools, libraries and APIs that enable GPU programming with ease.