What GPU do I need to run LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct?
7.8B parameters, published in F32. View on Hugging Face
Set up EXAONE-3.5-7.8B-InstructEXAONE-3.5-7.8B-Instruct is published by LGAI-EXAONE on Hugging Face, with 369,359 downloads and 159 likes to date. It's a ExaoneForCausalLM model built for text-generation, published natively in F32.
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 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run EXAONE-3.5-7.8B-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.
EXAONE-3.5-7.8B-Instruct: common questions
Can EXAONE-3.5-7.8B-Instruct run on a single GPU?
Yes, but not on a desktop card. At FP32 it needs 35.0 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 48.0 GB RTX 8000 on akash at $0.221/hr.
Can EXAONE-3.5-7.8B-Instruct run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 29.1 GB, or 35.0 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 14.6 GB, or 17.5 GB with overhead. That moves it onto a 24 GB card, which the FP32 weights do not fit. How much accuracy the cast costs is model-specific and is not measured here.
What is the least VRAM EXAONE-3.5-7.8B-Instruct can run in?
4.4 GB, at INT4 (quantized), which fits a 6 GB card, against 35.0 GB at FP32. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Does quantizing EXAONE-3.5-7.8B-Instruct lower the GPU bill?
Yes. At FP32 the cheapest live fit is one RTX 8000 on akash at $0.221/hr. At INT4 (quantized) it drops to one RTX 3060 on simplepod at $0.080/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More LGAI-EXAONE models
- 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)
- EXAONE-4.0-32B-FP8 (32.0B, F8_E4M3)
