AMD Developer

Meet AMD AI Developer Program Member Induwara Thenuka. Induwara completed the AI Developer Learning Path from AMD AI Academy and learned how to build AI agents, train and fine-tune models, and run LLMs on AMD GPUs. From his training, he built a multi-agent workspace using Ollama, where different AI agents work together to complete tasks. You can check out his project here: https://lnkd.in/eFdxH6ue He is looking forward to building more AI tools that people can actually use in daily tasks and automate things. Want to be featured or recognized? Submit your projects here: https://lnkd.in/eCaRHtHG

Advancing AI is where AMD shows what’s next and the Developer Track is created for the people actually building it. Our first featured speakers are live: 🔹 Matt White (The Linux Foundation / PyTorch Foundation) 🔹 Ying Sheng (RadixArk) 🔹 Simon Mo (Inferact / vLLM) Expect real talk on the tooling, infrastructure, and open-source momentum shaping how AI gets deployed at scale. More speakers and hands-on sessions will be announced soon. 👉 Register Now: https://lnkd.in/erEtZx26 📆 July 22–23, 2026 📍 San Francisco #AdvancingAI #AMDevs

“Local AI puts AI builders back in the driver’s seat.” Hugging Face CEO Clem Delangue on why local-first AI matters, and how AMD and Hugging Face are helping developers build faster with open-source AI.

Heading to Microsoft Build 👀 The AMD team will be onsite with hands-on labs and sessions covering: • OpenClaw agents with open-source models with Mahdi G. • Video generation workflows with Daniel Wang • Reinforcement learning + local AI on AMD laptops with Unsloth AI CEO Daniel Han • Building games with local AI agents with Ramakrishnan Sivakumar and Satya Devineni   If you’re at Build, come say hi 👋 Save these sessions to your agenda here: https://lnkd.in/gnEKCis4 #MSBuild #AMDevs

AI tutorials are everywhere. The challenge is finding a structured way to focus on what matters first. The AI Foundations learning path on AMD AI Academy helps developers explore AI, machine learning and GenAI concepts, while learning how to run and experiment with AI models on AMD platforms. Explore all the learning paths we offer: https://lnkd.in/e559S3Yq

We have been busy building and today we’re excited to share a new set of AI playbooks designed to help developers and builders get hands-on with local AI on AMD hardware. These hands-on guides walk through: → LLM fine-tuning and PEFT workflows using Google Gemma-3 on AMD Radeon™ GPUs. → Building a local multimodal AI stack using Lemonade, supporting LLMs, vision, image generation, and speech via Open WebUI → Fast SFT workflows using LoRA on AMD hardware with Unsloth Gemma-4E4B and the FineTome-100k dataset. → Low-latency translation pipelines using AMD ROCm + PyTorch across Ryzen AI and Radeon GPUs. → Running GenAI locally with Lemonade and switch between Vulkan, ROCm™, and NPU execution modes Whether you're just getting started or looking to push the boundaries of on-device AI, there's a playbook for you. More coming soon, explore the full library and start building today: https://lnkd.in/eqxzv4Cm

AI adoption is accelerating, and more developers are starting to think differently about where AI workloads should run. This blog explores the rise of the “Agent Computer” and why local AI inference is becoming an important part of the AI stack alongside the cloud. Why it matters: • More control over AI costs • Faster, lower-latency experiences • Greater privacy for sensitive workloads • More flexibility for continuous AI workflows The future of AI is hybrid. 👇 https://lnkd.in/d_dTqnPs

What do people misunderstand about AI right now? We asked #AMDevs to weigh in.

Learn what matters. Build what’s next. AMD AI Academy brings together developer-focused learning across agents, LLMs, inference, optimization, deployment, and more. Join the AMD AI Developer Program community and start exploring AMD AI Academy: https://lnkd.in/gUZDr4W8