Why Edge AI Models Fail on Real Hardware | Dr. Moritz Josef on AI Deployment

Most conversations around Edge AI focus on performance numbers, model size, and TOPS. But what happens when an AI model actually has to run on real edge hardware? In this episode of Beyond the Bit, Saharsh Singhania sits down with Dr. Moritz Josef, CEO and founder of Roofline, to discuss one of the biggest challenges in modern Edge AI: deploying AI models efficiently across fragmented hardware platforms. From PyTorch, TensorFlow, ONNX, and MLIR to NPUs, GPUs, CPUs, RISC-V, memory bottlenecks, heterogeneous SoCs, and compiler infrastructure, this conversation goes deep into why moving AI from cloud experiments to production-ready edge devices is still so difficult. The discussion explores why a model that works in the cloud can still fail on edge silicon, why TOPS alone does not define real AI performance, and why software stacks, memory hierarchy, and hardware-aware compilation are becoming critical for the future of Edge AI. If you are a developer, embedded engineer, AI hardware team, product builder, or anyone working on on-device intelligence, this episode breaks down the real deployment challenges behind Edge AI. In this episode, we discuss: -Why the Edge AI software stack is still fragmented -Why AI models often fail during real hardware deployment -How Roofline approaches model compilation across heterogeneous SoCs -The role of MLIR, ONNX, PyTorch, TensorFlow, CPUs, GPUs, and NPUs -Why TOPS is not the full story in AI performance -How memory bandwidth and data movement shape real-world AI efficiency -Why RISC-V and flexible hardware architectures matter for Edge AI -The future of AI model deployment at the edge 🎙️ Host: Saharsh Singhania 🎧 Guest: Dr. Moritz Josef, CEO & Founder, Roofline Podcast: Beyond the Bit Learn more about Ambient Scientific: ambientscientific.ai This is not just a conversation about AI models. It is a conversation about what it really takes to make intelligence work outside the cloud. Hashtags #edgeai #aihardware #BeyondTheBit #edgecomputing #aideployment #MLIR #ONNX #riscv #embeddedai #semiconductor #AICompiler #ondeviceai