👁 LG AI
LG AI
EXAONE 4.0 32B
Frontier
Jun 2025Released131K tokensContextEXAONE AILicense82 StrongQuality
EXAONE 4.0 32B (32B parameters) requires approximately 25.2 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 29 GB of VRAM.
Get started
— copy & paste to run locallyCopy-paste commands to run EXAONE 4.0 32B on your machine.
Run
ollama run exaone-4:32bQuick specs
Parameters32B
Architecturedense
Context131K tokens
Modalitytext
Min RAM12.5 GB
Rec. RAM19.5 GB (Q4_K_M)
LicenseEXAONE AI
FamilyEXAONE
✓ Code✓ Chat✓ Reasoning
About this model
- •Strong Korean-English bilingual performance
- •Competitive with Qwen and Llama at similar sizes on reasoning benchmarks
- •Fits on 24 GB VRAM at Q4_K_M
Your hardware
Detecting...
Quick picks
Best budgetA
Mac mini M4 64GB~$1,099 — 9 tok/s
Best overallS
NVIDIA A100 40GB~$10,000 — 72 tok/sBest hardware
Top picks for EXAONE 4.0 32B
Run this model
Quantization options
VRAM estimates by quant level
No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | — |
Q3_K_S | 3 | 15.7 GB | Low | — |
NVFP4 | 4 | 17.9 GB | Medium | — |
Q4_K_M | 4 | 19.5 GB | Medium | — |
Q5_K_M | 5 | 23.0 GB | High | — |
Q6_K | 6 | 26.2 GB | High | — |
Q8_0 | 8 | 34.2 GB | Very High | — |
F16 | 16 | 65.6 GB | Maximum | — |
Quality benchmarks
EXAONE 4.0 32B benchmark scores
Coding
SWE-bench Verified—
HumanEval+—
Aider Polyglot—
LiveCodeBench80.9%
Source: official · 2025-06-24
Hardware compatibility
Fit estimates across all hardware
Computing compatibility...
Memory breakdown
Reference: RTX 2060 6GB
Weights19.5 GB
KV Cache3.9 GB
Runtime1.2 GB
Headroom0.6 GB
Frequently asked questions
FAQ — EXAONE 4.0 32B
See also