LLM
How to deploy EXAONE-3.5-32B-Instruct on a GPU cloud
A 32B language model for chat and instruction-following. Full specs, license and use cases.
EXAONE-3.5-32B-Instruct size and hardware requirements
32.0B
Total parameters
Dense (no MoE)
Architecture
F32
Published precision
143.1 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP32 | 119.2 GB | 143.1 GB | V100 | 5 | $0.850/hr |
| FP8 (quantized) | 29.8 GB | 35.8 GB | RTX 6000 Ada | 1 | $0.524/hr |
| INT4 (quantized) | 14.9 GB | 17.9 GB | RTX 3090 | 1 | $0.147/hr |
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 pod