GPU VRAM guide for local AI

Last updated: 2026-07-26

Answer: more VRAM means larger models and longer context. For most hobby local LLM users, 24GB is the target. 16GB works for mid-size models. 12GB is entry. 32GB (5090) and 48GB workstation cards open bigger models with fewer compromises.

8–12GB

Small quantized chat models and light image gen. Cards: 5060, 5070, 3060 12GB, 4070, 3080 10/12GB.

16GB

Mid-size models. Cards: 5070 Ti, 5080, 5060 Ti 16GB, 4070 Ti Super, 4080, RX 9070 XT, 7800 XT.

20–24GB

Common consumer target for local AI. Cards: 3090, 4090, 7900 XTX, 7900 XT (20GB).

32GB

Consumer step above 24GB without a workstation SKU. Card: RTX 5090.

48GB+

Large models, multi-modal, fewer compromises. Cards: RTX A6000, RTX 6000 Ada. Thin volume, high ticket, eBay-heavy.

Quantization note

Q4/Q5 quantization shrinks memory needs. A 24GB card can run models that would not fit at full precision. Benchmarks change with model releases — re-check VRAM claims when a new open model ships.

FAQ

How much VRAM do I need for local AI?

More VRAM means larger models and longer context. For most hobby local LLM users, 24GB is the target. 16GB is workable. 12GB is entry-level. 48GB is workstation territory.

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