Discover how AMD’s ROCm software stack unlocks data center-class ML performance on embedded integrated GPUs, enabling diverse AI workloads from CNNs and transformers to VLMs and LLMs in power-constrained edge environments. In this technical session, we show how ROCm, HIP (Heterogeneous-computing Interface for Portability) and hybrid iGPU + NPU architectures on Ryzen AI platforms deliver production-ready inference for physical AI applications spanning autonomous systems, industrial automation and intelligent edge devices. Learn how to leverage AMD’s open-source ecosystem to optimize any ML model topology for embedded deployment while maintaining the flexibility and programmability of full GPU compute. Through performance benchmarks across multiple model architectures, architectural deep dives and demonstrations, attendees will gain practical insights into building efficient AI pipelines that scale from vision models to multimodal AI.