LGAI-EXAONE
EXAONE 4.0 32B (32B parameters) requires approximately 25.1 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.
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Quantization options
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 | — |
Hardware compatibility
Computing compatibility...
Memory breakdown
Frequently asked questions
EXAONE 4.0 32B (32B parameters) requires approximately 25.1 GB of VRAM with Q4_K_M quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, Mac mini M4 64GB can run EXAONE 4.0 32B with a compatibility score of 48/100. It provides 64 GB of memory and achieves approximately 8.0 tokens per second.
The recommended quantization for EXAONE 4.0 32B is Q4_K_M, which offers the best balance between model quality and memory efficiency. Higher quantizations preserve more quality but require more VRAM.
The top recommended hardware for EXAONE 4.0 32B: NVIDIA A100 40GB (score: 56/100), RTX PRO 5000 Blackwell 48GB (score: 53/100), MacBook Pro M4 Max 64GB (score: 53/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, EXAONE 4.0 32B is well-suited for chat. It was designed with these use cases in mind.
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