AI Hardware and Components

A local AI build typically requires a compatible CPU, GPU, sufficient system memory, storage, motherboard, power supply, and cooling. The appropriate hardware depends on the models you plan to run and whether you're performing inference, training, or development.

VRAM requirements depend on the model's size, quantization, context length, and workload. Smaller models may run on GPUs with 8GB to 12GB of VRAM, while larger models can require 24GB, 48GB, or more. Higher VRAM capacity generally allows you to run larger models locally.

For many local AI workloads, particularly large language model inference and training, GPU performance and VRAM capacity are primary considerations. CPUs remain important for system operations, data preparation, and workloads that aren't GPU-accelerated.

The amount of system RAM you need depends on your workload, model size, and GPU configuration. A system with 32GB of RAM can be a starting point for many local AI applications, while larger models, development environments, and multitasking may benefit from 64GB or more.

Yes. Many existing desktop PCs can be upgraded for local AI by adding a compatible GPU, increasing RAM, or expanding storage. Before upgrading, check your motherboard compatibility, available PCIe slots, power supply capacity, case clearance, and cooling.

Yes. Micro Center carries GPUs, CPUs, motherboards, memory, storage, power supplies, cooling solutions, and other components for AI workstation builds. Our in-store associates can help you select compatible hardware for your intended workloads.