by Meta AI

Llama 3.2 90B Vision.

vision open weights workstation 90B params 128K ctx
Smallest GPU
1× Nvidia A16
Run it yourself

Cheapest hardware per quantisation.

Each row is one quantisation tier (the same weights compressed differently). Lower precision → lower VRAM → cheaper hardware, at the cost of small accuracy loss. $/hr refreshed hourly from each provider's API.

Quantisation Cheapest GPU config Total VRAM Live $/hr tokens/sec
FP16
FP16 — half precision (default)
256 GB Compare →
FP8
FP8 — 8-bit float (Hopper / Blackwell)
141 GB Compare →
INT4
INT4 — 4-bit integer (~4× VRAM saving)
64 GB Compare →
Just need an API?
Skip the GPU rental and call a hosted endpoint instead. See access providers on the Overview tab →