What Hardware Generative Models Actually Need
The question «which graphics card do I need» has an unobvious answer: what matters most is not speed but the amount of video memory. A model either fits into memory entirely or does not work — there is almost no middle state.
Why memory beats speed
A fast card with little memory hits a wall: the model will not load, and no amount of performance helps. A slow card with plenty of memory will manage — just more slowly. The difference between «slow» and «not at all» is fundamental.
Capacity benchmarks
- 8 gigabytes — image generation at moderate resolution, basic tasks
- 12–16 gigabytes — comfortable work, training small additions, upscaling
- 24 gigabytes and above — video, training, high-resolution work without contortions
System memory and storage
System RAM is best at twice the video memory: models load through it. Storage should be solid-state and generous — model collections run to tens of gigabytes, and intermediate files grow fast when working with video.
On cooling
Generation loads a card continuously rather than in bursts as games do. A case with considered airflow extends hardware life far more than people assume. An overheating card drops its clocks, and performance falls with them.
Is it worth buying
Count by workload. Occasional tasks are cheaper solved by renting a remote machine: you pay for hours rather than for idle hardware. Constant work pays back a purchase within months — and your material never leaves your computer, which for sensitive shoots often matters more than money.
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