{"repo":"arm-education/AI-on-Arm","free":true,"listed":false,"github":"https://github.com/arm-education/AI-on-Arm","clone":"git clone https://github.com/arm-education/AI-on-Arm.git","description":"Hands-on course materials for deploying and optimizing generative AI on Arm processors: Raspberry Pi, AWS Graviton, SIMD, quantization (educational)","language":"Jupyter Notebook","stars":34,"topics":["ai-inference","arm","aws-graviton","cloud-computing","edge-computing","genai","generative-ai","jupyter-notebook","machine-learning","neon"],"license":null,"category":"ai-agents","readme_excerpt":"Optimizing Generative AI on Arm Processors Important To download the latest stable version, please click below instead of using the \"Download ZIP\" button. Click here to download Welcome to Optimizing Generative AI on Arm Processors , a hands-on course designed to help you optimize generative AI workloads on Arm architectures. Through practical labs and structured lectures, you will learn how to deploy AI models efficiently across different Arm-based environments. Course Structure This course consists of three hands-on labs and four lectures. Hands-On Labs - Lab 1 : Optimizing generative AI on mobile devices, such as the Raspberry Pi 5. - Lab 2 : Deploying AI workloads on Arm-based cloud servers, including AWS Graviton. - Lab 3 : Comparing Cloud vs. Edge inference, analyzing challenges and trade-offs. Lecture Series Inside the slides/ folder, you will find four lectures covering the key concepts and challenges in AI inference on Arm: 1. Challenges Facing Cloud and Edge GenAI Inference – Understanding the limitations and constraints of AI inference in different environments. 2. Generative AI Models – Exploring model architectures, training methodologies, and deployment considerations. 3. ML Frameworks and Optimized Libraries – A deep dive into AI software stacks, including PyTorch, ONNX Runtime, and Arm-specific optimizations. 4. Optimization for CPU Inference – Techniques such as quantization, pruning, and leveraging SIMD instructions for faster AI performance. What You'll Lea","default_branch":null,"files":null,"tree":[],"storefront":"/r/arm-education","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/arm-education/AI-on-Arm/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}