Server needs vary depending on the AI phase: Training: Demands the most resources (high-end GPUs, large RAM). Inference: Requires less power than training, but still needs optimized hardware. In an AI server, it is used by the application, containers, queues, vector database, cache, documents and possible offloading of part of the data from the GPU. For a test server, you can start with 128–256 GB of RAM. For a production service with document search, it is better to plan for 256–512 GB. Deciding on your AI hardware setup can seem daunting, but a methodical process in selecting and configuring appropriate hardware can guarantee success. AI model size, complexity, and the volume of data all drastically affect server requirements. Larger, more complex models, trained on massive. This comprehensive guide aims to demystify the intricacies of server hardware for AI, providing a detailed comparison of CPUs, GPUs, and RAM. We will explore their architectural differences, their respective strengths and weaknesses in handling various AI tasks, and how to optimally configure them. To determine your AI system requirements on VPS, first, you should understand whether your AI model is CPU-based or GPU-based, since some AI models depend on the CPU, while others, like Generative AI, depend on the GPU.