Max And Omnis Q4_XS 64B Parameters

VRAM Requirements for
Nemotron-3-Super-64B-A12B-Math-REAP-GGUF Q4_XS

To run Nemotron-3-Super-64B-A12B-Math-REAP-GGUF locally at Q4_XS quantization, you need at minimum 39.05 GB of GPU VRAM.

39.05 GB
Required VRAM
36 GB
File Size
8K tokens
Context Window
64B
Parameters
Estimated VRAM Required
39.05
GB
Prosumer / Workstation
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $1,200 – $1,800

2× RTX 3090 (48GB total)

48 GB

Dual-GPU via tensor parallelism. Best cost per GB at this tier.

Balanced $4,000 – $5,500

NVIDIA A6000 (48GB)

48 GB

Single-card 48GB pro GPU. Clean setup, no multi-GPU overhead.

Ultimate $8,000 – $12,000

NVIDIA A100 40GB SXM

40 GB HBM2e

Data-centre HBM2e bandwidth. Dramatically faster throughput.

📊 VRAM Calculation Breakdown

Model File Size (Q4_XS) 36 GB
Context Overhead (8,192 tokens × 64B × 2 ÷ 1M) 1.049 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 39.05 GB

Try a Different Quantization

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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 Current
39.05 GB
Q6_K 52 GB file
55.05 GB
Q8_0 68 GB file
71.05 GB
BF16 128 GB file
131.05 GB
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Frequently Asked Questions

Can I run Nemotron-3-Super-64B-A12B-Math-REAP-GGUF Q4_XS on a consumer GPU?
Running Nemotron-3-Super-64B-A12B-Math-REAP-GGUF Q4_XS locally requires 39.05 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 Q4_XS quality good enough for production?
Q4_K_M is an excellent balance of quality and performance. Perplexity tests show minimal degradation (< 2%) vs FP16 for most models. Suitable for most production applications.