What GPU do I need to run LGAI-EXAONE/EXAONE-3.5-32B-Instruct?
A 32B language model for chat and instruction-following. 32.0B parameters, published in F32. View on Hugging Face
EXAONE-3.5-32B-Instruct is published by LGAI-EXAONE on Hugging Face, with 36,125 downloads and 130 likes to date. It's a ExaoneForCausalLM model built for text-generation, published natively in F32.
What EXAONE-3.5-32B-Instruct is
EXAONE-3.5-32B-Instruct is a 32B-parameter language model published by LG AI Research on Hugging Face. It is released under Custom license.
License note: a lab-specific license (tagged "other" on Hugging Face); read the model card's own license section before commercial use. Facts in this section are sourced from EXAONE-3.5-32B-Instruct's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Chat assistants
- Instruction following
- Synthetic data generation
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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP32 | 119.2 GB | 143.1 GB | V100 | 5 | $0.935/hr |
| FP8 (quantized) | 29.8 GB | 35.8 GB | RTX 4090 | 1 | $0.441/hr |
| cheaper alt. | RTX 5060 Ti | 3 | $0.330/hr | ||
| INT4 (quantized) | 14.9 GB | 17.9 GB | RTX A5000 | 1 | $0.176/hr |
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-3.5-32B-Instruct at its published (F32) precision: 5× V100, at $0.187/hr per GPU ($0.935/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
EXAONE-3.5-32B-Instruct: common questions
Can EXAONE-3.5-32B-Instruct run on a single GPU?
No. At FP32 it needs 143.1 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 32.0 GB V100, and it takes 5 of them.
Can EXAONE-3.5-32B-Instruct run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 119.2 GB, or 143.1 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 59.6 GB, or 71.5 GB with overhead. How much accuracy the cast costs is model-specific and is not measured here.
How many GPUs do I need to run EXAONE-3.5-32B-Instruct?
5 at FP32. It needs 143.1 GB of VRAM and the cheapest capable live offer is a 32.0 GB V100, so 5 of them come to $0.935/hr in total.
What is the least VRAM EXAONE-3.5-32B-Instruct can run in?
17.9 GB, at INT4 (quantized), which fits a 24 GB card, against 143.1 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.
How to run EXAONE-3.5-32B-Instruct
Run EXAONE-3.5-32B-Instruct with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve LGAI-EXAONE/EXAONE-3.5-32B-Instruct --tensor-parallel-size 5Run EXAONE-3.5-32B-Instruct with Ollama
Verified against Ollama's own library listing.
ollama run exaone3.5:32bRun EXAONE-3.5-32B-Instruct with GGUF quantizations
Prebuilt GGUF weights published at bartowski/EXAONE-3.5-32B-Instruct-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf bartowski/EXAONE-3.5-32B-Instruct-GGUFSource: https://huggingface.co/bartowski/EXAONE-3.5-32B-Instruct-GGUF
Deploy EXAONE-3.5-32B-Instruct on Aquanode
Aquanode has no one-click deploy template for EXAONE-3.5-32B-Instruct; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (5× V100 or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU podWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More EXAONE 3.5 models
- EXAONE-3.5-7.8B-Instruct (7.8B, F32)
- EXAONE-3.0-7.8B-Instruct (7.8B, F32)
- EXAONE-3.5-2.4B-Instruct (2.4B, F32)
Related reading: V100 pricing and specs, and The best GPUs for AI, ranked.
