Why AI Needs GPUs: A No-Code Beginner’s Guide to Infrastructure

Part 4 of the "Learn AI With Me: No Code" series explores the importance of GPUs over CPUs in AI tasks, emphasizing their ability to handle massive parallel computations essential for AI workloads, such as matrix math and vector operations. While CPUs are optimized for sequential processing, GPUs excel in performing numerous small calculations simultaneously, making them indispensable for training and running large AI models. The text also delves into the specifics of different GPU models, such as the 3090, 4090, A100, and H100, highlighting factors like VRAM, tensor cores, and architecture that influence their performance. Additionally, the concept of using pre-configured templates on platforms like Runpod is introduced, allowing users to quickly set up AI models with minimal configuration effort. The article touches on the advantages of cloud GPUs over local hardware, such as scalability and cost-effectiveness, and introduces Serverless GPU endpoints as a hassle-free option for model inference.