Before you begin

Hardware questions.

Check the requirements for the exact course you want. A graphics card's memory, processing power, system RAM, and free storage all affect what is practical.

What computer do the local lessons use?

The current Home Studio reference computer runs Windows 11 with an NVIDIA RTX 4090, 24 GB of video memory (VRAM), and 64 GB of system memory. This is the machine used to develop the lessons. It is not a verified minimum for every module. Required free storage and support for other GPUs are still being measured.

What does VRAM change?

VRAM is working space for a model and the data it processes. More VRAM can make larger workflows practical and reduce transfers to slower system memory. It does not set a fixed video length or guarantee better-looking output. Resolution, frame count, model, settings, and memory-saving techniques all matter.

What does processing power change?

Processing power mostly affects the time needed for a result. Architecture, memory bandwidth, and software support matter alongside the number of cores. An RTX 3090 and 4090 both have 24 GB VRAM; that does not prove the 4090 finishes every AI task in half the time. We will publish timings measured on the course workflow.

What if I do not have a powerful GPU?

Browser courses run generation on a service and usually charge credits or subscription fees. Rented-GPU courses run generation on a remote machine and charge for compute and possibly storage or bandwidth. Your local device still needs a current browser or remote client, secure internet connection, and enough space for downloaded results. Even a very old computer may work for some remote tasks, but compatibility depends on its current software, not age alone.