Unsloth Q5_K_S 120B Parameters

VRAM Requirements for
NVIDIA-Nemotron-3-Super-120B-A12B-GGUF Q5_K_S

To run NVIDIA-Nemotron-3-Super-120B-A12B-GGUF locally at Q5_K_S quantization, you need at minimum 86.47 GB of GPU VRAM.

86.47 GB
Required VRAM
82.5 GB
File Size
8K tokens
Context Window
120B
Parameters
Estimated VRAM Required
86.47
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

Balanced $60,000+

2× H100 80GB NVLink

160 GB HBM3

NVLink bridge enables unified 160GB VRAM pool.

Ultimate $3,000,000+

NVIDIA GB200 NVL72

1.4 TB HBM3e

Next-gen Grace Blackwell Superchip. Built for frontier models.

📊 VRAM Calculation Breakdown

Model File Size (Q5_K_S) 82.5 GB
Context Overhead (8,192 tokens × 120B × 2 ÷ 1M) 1.966 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 86.47 GB

Try a Different Quantization

Use the interactive calculator to compare NVIDIA-Nemotron-3-Super-120B-A12B-GGUF across all available formats.

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NVIDIA-Nemotron-3-Super-120B-A12B-GGUF — Other Quantizations

Q2_K_XL 37.5 GB file
41.47 GB
Q2_M 37.5 GB file
41.47 GB
Q2_XXS 37.5 GB file
41.47 GB
Q3_K_M 52.5 GB file
56.47 GB
Q3_K_S 52.5 GB file
56.47 GB
Q3_K_XL 52.5 GB file
56.47 GB
Q3_S 52.5 GB file
56.47 GB
Q3_XXS 52.5 GB file
56.47 GB
Q4_K_M 67.5 GB file
71.47 GB
Q4_K_S 67.5 GB file
71.47 GB
Q4_K_XL 67.5 GB file
71.47 GB
Q4_NL 67.5 GB file
71.47 GB
Q4_XS 67.5 GB file
71.47 GB
Q5_K_XL 82.5 GB file
86.47 GB
Q5_K_S Current
86.47 GB
Q5_K_M 82.5 GB file
86.47 GB
Q6_K 97.5 GB file
101.47 GB
Q6_K_XL 97.5 GB file
101.47 GB
FP4 120 GB file
123.97 GB
Q1_M 120 GB file
123.97 GB
Q8_0 127.5 GB file
131.47 GB
Q8_K_XL 127.5 GB file
131.47 GB
BF16 240 GB file
243.97 GB
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Frequently Asked Questions

Can I run NVIDIA-Nemotron-3-Super-120B-A12B-GGUF Q5_K_S on a consumer GPU?
Running NVIDIA-Nemotron-3-Super-120B-A12B-GGUF Q5_K_S locally requires 86.47 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 NVIDIA-Nemotron-3-Super-120B-A12B-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 Q5_K_S quality good enough for production?
Q5_K_S is suitable for specialized use cases. Check community benchmarks for specific quality metrics.