Can I run EXAONE 4.5 33B?

EXAONE 4.5 33B is LG AI Research's vision-and-reasoning model, and at 33B dense parameters it sits firmly in the heavyweight tier of what you can self-host. A 4-bit quant lands around 20 GB, so it will not fit on a 12 GB card like an RTX 3060 at all; you need a 24 GB GPU such as a 4090, or a Mac with plenty of unified memory. The minimum to load it sensibly is about 32 GB of RAM. This is not a casual laptop model. It is for people who want a capable multimodal assistant and have the hardware to back it.

In daily use on a 4090 it runs around 43 tokens per second at 4-bit, which is comfortable for chat and reading images, though noticeably heavier than a small 8B model. On an Apple M Max you are looking at roughly 17 tokens per second, usable but more deliberate, and on CPU with DDR5 it drops to about 3 tokens per second, which is patience-testing. The 256K context window is large, but memory is the catch: even at 128K context the total footprint climbs to about 51.7 GB, so a 24 GB card forces you to keep working context modest.

Against Qwen3-VL 32B, the closest comparison in its class, EXAONE generally trades blows rather than dominating; Qwen3-VL tends to have broader tooling and quant availability, while Qwen 3 32B is the lighter pick if you only need text reasoning. EXAONE's standout trait is being a genuinely strong vision-plus-reasoning model in one package from a major lab. The important caveat is the license: it ships under the EXAONE License, which is non-commercial, so you cannot use it in production or any commercial product. Keep it to research, prototyping, and personal use.