GPU
7 guides tagged “GPU”.
- NPU vs GPU: What's the Difference? An NPU is a fixed-function accelerator for low-power inference; a GPU is programmable and built for training and flexible serving.
- What Is a TOPS Score (and What Does It Actually Measure)? TOPS is a peak throughput figure that depends on precision, sparsity, and memory. It is not a measure of real model performance.
- Hardware for AI Builders: How the Machine Your Code Runs On Actually Works A zero-jargon guide to computer hardware for people who build with AI: what a CPU, RAM, SSD, GPU, and data-centre server actually do, and how to size a machine for your app.
- What Is an NPU (and Do You Need an 'AI PC')? NPUs are small fixed-function AI accelerators now shipping in laptops and phones. Learn what they are good at, what TOPS means, and why they are not a substitute for a GPU.
- GPU vs CPU for AI: When Is a GPU Actually Worth It? Training needs GPUs, inference often does not, and an API call beats both for most small projects. A cost and capability comparison for AI builders.
- What Is a GPU (and Why Is It Different From a CPU)? A GPU is thousands of simple cores built to do the same operation on huge blocks of numbers at once. Learn why that is exactly what AI needs and when it is the wrong tool.
- What Is VRAM (and Why Does a Model Not Fit)? GPU memory decides which AI models you can run. Learn the arithmetic for weights, cache, and context, and the practical ways to make a model fit.