Max And Omnis BF16 64B Parameters

VRAM Requirements for
Nemotron-3-Super-64B-A12B-Math-REAP-GGUF BF16

To run Nemotron-3-Super-64B-A12B-Math-REAP-GGUF locally at BF16 quantization, you need at minimum 131.05 GB of GPU VRAM.

131.05 GB
Required VRAM
128 GB
File Size
8K tokens
Context Window
64B
Parameters
Estimated VRAM Required
131.05
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 (BF16) 128 GB
Context Overhead (8,192 tokens × 64B × 2 ÷ 1M) 1.049 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 131.05 GB

Try a Different Quantization

Use the interactive calculator to compare Nemotron-3-Super-64B-A12B-Math-REAP-GGUF across all available formats.

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Nemotron-3-Super-64B-A12B-Math-REAP-GGUF — Other Quantizations

Q2_M 20 GB file
23.05 GB
Q3_M 28 GB file
31.05 GB
Q4_K_M 36 GB file
39.05 GB
Q4_XS 36 GB file
39.05 GB
Q6_K 52 GB file
55.05 GB
Q8_0 68 GB file
71.05 GB
BF16 Current
131.05 GB
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Frequently Asked Questions

Can I run Nemotron-3-Super-64B-A12B-Math-REAP-GGUF BF16 on a consumer GPU?
Running Nemotron-3-Super-64B-A12B-Math-REAP-GGUF BF16 locally requires 131.05 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-64B-A12B-Math-REAP-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 BF16 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.