EXAONE 3.5 32B — VRAM Requirements
Written by Jakub Rusinowski · Last updated July 21, 2026
How much GPU VRAM you need to run EXAONE 3.5 EXAONE 3.5 32B by LG AI Research locally, a 32B-parameter model. Figures are quantized weights + KV cache + framework overhead from a memory-bandwidth roofline model.
EXAONE 3.5 32B needs about 20 GB VRAM at Q4_K_M.
VRAM by Quantization
| Quant | Bits/weight | Weights | Total VRAM |
|---|---|---|---|
| Q2_K | 2.63 | 10.5 GB | 11.3 GB |
| Q3_K_M | 3.41 | 13.6 GB | 14.4 GB |
| Q4_K_M | 4.83 | 19.3 GB | 20.1 GB |
| Q5_K_M | 5.67 | 22.7 GB | 23.5 GB |
| Q6_K | 6.56 | 26.2 GB | 27.0 GB |
| Q8_0 | 8.50 | 34.0 GB | 34.8 GB |
| F16 | 16.00 | 64.0 GB | 64.8 GB |
Switch quantization in the interactive calculator, or see the full EXAONE 3.5 model page.
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Model creators: paste this into your Hugging Face model card README to link readers straight to this VRAM breakdown.
[](https://llmconfigurator.com/en/vram-calculator/exaone-3.5-32b)Estimates only — actual VRAM varies with context length, batch size, runtime and KV-cache settings.