Unsloth Q1_S 49B Parameters

VRAM Requirements for
Llama-3_3-Nemotron-Super-49B-v1_5-GGUF Q1_S

To run Llama-3_3-Nemotron-Super-49B-v1_5-GGUF locally at Q1_S quantization, you need at minimum 51.8 GB of GPU VRAM.

51.8 GB
Required VRAM
49 GB
File Size
8K tokens
Context Window
49B
Parameters
Estimated VRAM Required
51.8
GB
Data Centre Class
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 3.8 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 (Q1_S) 49 GB
Context Overhead (8,192 tokens × 49B × 2 ÷ 1M) 0.803 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 51.8 GB

Try a Different Quantization

Use the interactive calculator to compare Llama-3_3-Nemotron-Super-49B-v1_5-GGUF across all available formats.

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Llama-3_3-Nemotron-Super-49B-v1_5-GGUF — Other Quantizations

Q2_K 15.31 GB file
18.11 GB
Q2_K_L 15.31 GB file
18.11 GB
Q2_K_XL 15.31 GB file
18.11 GB
Q2_M 15.31 GB file
18.11 GB
Q2_XXS 15.31 GB file
18.11 GB
Q3_K_M 21.44 GB file
24.24 GB
Q3_K_S 21.44 GB file
24.24 GB
Q3_K_XL 21.44 GB file
24.24 GB
Q3_XXS 21.44 GB file
24.24 GB
Q4_XS 27.56 GB file
30.36 GB
Q4_NL 27.56 GB file
30.36 GB
Q4_K_XL 27.56 GB file
30.36 GB
Q4_K_S 27.56 GB file
30.36 GB
Q4_K_M 27.56 GB file
30.36 GB
Q4_1 27.56 GB file
30.36 GB
Q4_0 27.56 GB file
30.36 GB
Q5_K_M 33.69 GB file
36.49 GB
Q5_K_S 33.69 GB file
36.49 GB
Q5_K_XL 33.69 GB file
36.49 GB
Q6_K 39.81 GB file
42.61 GB
Q6_K_XL 39.81 GB file
42.61 GB
Q1_M 49 GB file
51.8 GB
Q1_S Current
51.8 GB
Q8_0 52.06 GB file
54.86 GB
Q8_K_XL 52.06 GB file
54.86 GB
BF16 98 GB file
100.8 GB
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Frequently Asked Questions

Can I run Llama-3_3-Nemotron-Super-49B-v1_5-GGUF Q1_S on a consumer GPU?
Running Llama-3_3-Nemotron-Super-49B-v1_5-GGUF Q1_S locally requires 51.8 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 Llama-3_3-Nemotron-Super-49B-v1_5-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 Q1_S quality good enough for production?
Q1_S is suitable for specialized use cases. Check community benchmarks for specific quality metrics.