Yeonwoo Sung FP16 32B Parameters

VRAM Requirements for
EXAONE-Deep-32B-Q4_K_M-GGUF FP16

To run EXAONE-Deep-32B-Q4_K_M-GGUF locally at FP16 quantization, you need at minimum 66.52 GB of GPU VRAM.

66.52 GB
Required VRAM
64 GB
File Size
8K tokens
Context Window
32B
Parameters
Estimated VRAM Required
66.52
GB
Data Centre Class
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 18.5 GB short
Budget $3,200 – $4,000

2× RTX 4090 (48GB) + aggressive quant

48 GB

Use a lower quantization to fit. Viable for testing at this scale.

Balanced $15,000 – $22,000

NVIDIA A100 80GB PCIe

80 GB HBM2e

Single-card 80GB. Industry-standard for large model inference.

Ultimate $25,000 – $40,000

NVIDIA H100 80GB SXM5

80 GB HBM3

State-of-the-art inference. 3× the bandwidth of A100.

📊 VRAM Calculation Breakdown

Model File Size (FP16) 64 GB
Context Overhead (8,192 tokens × 32B × 2 ÷ 1M) 0.524 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 66.52 GB

Try a Different Quantization

Use the interactive calculator to compare EXAONE-Deep-32B-Q4_K_M-GGUF across all available formats.

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EXAONE-Deep-32B-Q4_K_M-GGUF — Other Quantizations

Q4_0 18 GB file
20.52 GB
FP16 Current
66.52 GB
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Frequently Asked Questions

Can I run EXAONE-Deep-32B-Q4_K_M-GGUF FP16 on a consumer GPU?
Running EXAONE-Deep-32B-Q4_K_M-GGUF FP16 locally requires 66.52 GB VRAM, which exceeds consumer GPUs. You'll need prosumer cards like the NVIDIA A6000 (48GB) or an A100 (80GB).
What happens if I don't have enough VRAM?
If your GPU VRAM is insufficient, llama.cpp and similar tools will offload model layers to system RAM (CPU inference). This is much slower — expect 10-50× the generation latency compared to full GPU inference.
Can I use multiple GPUs to run EXAONE-Deep-32B-Q4_K_M-GGUF?
Yes! Tools like llama.cpp, vLLM, and Ollama support tensor parallelism across multiple GPUs. For example, 2× RTX 3090 (24GB each) gives you 48GB total VRAM, which can run many large models.
Is FP16 quality good enough for production?
FP16/BF16 is the standard precision used for production inference and serves as the quality baseline. All fine-tuned models are typically served at this precision.