Blobbybob Q8_0 120B Parameters

VRAM Requirements for
Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled Q8_0

To run Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled locally at Q8_0 quantization, you need at minimum 131.47 GB of GPU VRAM.

131.47 GB
Required VRAM
127.5 GB
File Size
8K tokens
Context Window
120B
Parameters
Estimated VRAM Required
131.47
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 (Q8_0) 127.5 GB
Context Overhead (8,192 tokens × 120B × 2 ÷ 1M) 1.966 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 131.47 GB

Try a Different Quantization

Use the interactive calculator to compare Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled across all available formats.

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Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled — Other Quantizations

Q4_0 67.5 GB file
71.47 GB
Q8_0 Current
131.47 GB
FP16 240 GB file
243.97 GB
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Frequently Asked Questions

Can I run Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled Q8_0 on a consumer GPU?
Running Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled Q8_0 locally requires 131.47 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 Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled?
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 Q8_0 quality good enough for production?
Q8_0 produces near-lossless quality compared to FP16. It's widely used in production deployments where quality is critical and you can afford the extra VRAM.