What GPU do I need to run LGAI-EXAONE/EXAONE-4.0-1.2B?
1.3B parameters, published in BF16. View on Hugging Face
EXAONE-4.0-1.2B is published by LGAI-EXAONE on Hugging Face, with 20,365 downloads and 190 likes to date. It's a Exaone4ForCausalLM model built for text-generation, published natively in BF16.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 caveat: requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run EXAONE-4.0-1.2B at its published (BF16) precision: 1× RTX 3070 on simplepod, at $0.050/hr per GPU ($0.050/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More LGAI-EXAONE models
- EXAONE-3.5-7.8B-Instruct (7.8B, F32)
- EXAONE-3.5-2.4B-Instruct (2.4B, F32)
- EXAONE-3.5-32B-Instruct (32.0B, F32)
- EXAONE-4.0-32B (32.0B, BF16)
- K-EXAONE-236B-A23B (237.1B, BF16)
- EXAONE-4.0-32B-FP8 (32.0B, F8_E4M3)
