Microsoft and AMD continue to collaborate enabling and accelerating AI workloads across AMD GPUs on Windows platforms. Now we are happy to share that with ‘Automatic1111 DirectML extension’ preview from Microsoft, you can run Stable Diffusion 1.5 with base Automatic1111 with similar upside across AMD GPUs mentioned in our previous post

AMD is excited to kick off the Pervasive AI Developer Contest; a competition designed to spark innovation amongst AI developers.  Showcase your creativity and technical expertise on chosen AMD platforms and participate in one of three categories: Generative AI, Robotics AI, and PC AI.

AMD continues to give the AI community more solutions options with the AMD ROCm software and the support of now three high-end RDNA™ 3 architecture-based GPUs for Machine Learning development using PyTorch.

One of the most immediately impactful applications of AI in the enterprise happens to be one of the first skills I personally wanted to develop – writing and editing. I took journalism and copywriting classes, relished exams in essay form, and learned copyediting marks. Today, as a marketer and communicator at one of the few companies in the world that can power AI in the enterprise from the data center to the PC, I find myself strongly advocating for team members to incorporate AI in their editing, business communications, and copy writing.

Did you know you can enable Llama2 with Microsoft Olive under Microsoft DirectML? Check out this "How-to" guide on instructions to set up and use with Radeon RX Graphics

I’m excited to share that today, AMD has completed the acquisition of Nod.ai - further expanding our open-source AI software capabilities. We are thrilled to welcome Nod.ai’s talented team of AI software experts to the AMD Artificial Intelligence Group (AIG).   

Turn your PC or workstation into a Machine Learning development platform with AMD Radeon GPUs

Researchers and developers working with Machine Learning (ML) models and algorithms using PyTorch can now use AMD ROCm 5.7 on Ubuntu® Linux® to tap into the parallel computing power of the Radeon™ RX 7900 XTX and the Radeon™ PRO W7900 graphics cards which are based on the AMD RDNA™ 3 GPU architecture.

With the latest update to DaVinci Resolve 18.6, the AMD and Blackmagic Design engineering teams closely collaborated to enable an optimized machine learning pipeline for running AI workloads on AMD hardware, leading to a significant performance boost to several community favorite AI features.

Right now, the tech world is awash in discussion of AI. Huge data center chips with terabytes of bandwidth and multi-thousand-dollar price tags share the space with diminutive Internet of Things products designed for ultra-low power processing. The news cycle serves up an endless discussion of artificial intelligence, but many of these discussions don't explain the difference between platforms and capabilities. It's all well and good to say AMD has built an "integrated AI engine," but what does that actually mean and why should you care? What's the advantage to you?

The launch of Ryzen AI was a first for x86 processors and an investment in the future of computing, but why integrate an AI engine in the first place?