What GPU do I need to run LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct?
7.8B parameters, published in F32. View on Hugging FaceGated
EXAONE-3.0-7.8B-Instruct is published by LGAI-EXAONE on Hugging Face, with 11,956 downloads and 421 likes to date. It's a ExaoneForCausalLM model built for text-generation, published natively in F32, and gated: you'll need to accept the model's terms on Hugging Face before downloading weights.
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-3.0-7.8B-Instruct at its published (F32) precision: 1× RTX 8000 on akash, at $0.221/hr per GPU ($0.221/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-4.0-1.2B (1.3B, BF16)
