Unsloth Q4_K_M 253B Parameters

VRAM Requirements for
Llama-3_1-Nemotron-Ultra-253B-v1-GGUF Q4_K_M

To run Llama-3_1-Nemotron-Ultra-253B-v1-GGUF locally at Q4_K_M quantization, you need at minimum 148.46 GB of GPU VRAM.

148.46 GB
Required VRAM
142.31 GB
File Size
8K tokens
Context Window
253B
Parameters
Estimated VRAM Required
148.46
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 (Q4_K_M) 142.31 GB
Context Overhead (8,192 tokens × 253B × 2 ÷ 1M) 4.145 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 148.46 GB

Try a Different Quantization

Use the interactive calculator to compare Llama-3_1-Nemotron-Ultra-253B-v1-GGUF across all available formats.

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Llama-3_1-Nemotron-Ultra-253B-v1-GGUF — Other Quantizations

Q2_K 79.06 GB file
85.21 GB
Q2_K_L 79.06 GB file
85.21 GB
Q2_K_XL 79.06 GB file
85.21 GB
Q2_M 79.06 GB file
85.21 GB
Q2_XXS 79.06 GB file
85.21 GB
Q3_K_M 110.69 GB file
116.84 GB
Q3_K_S 110.69 GB file
116.84 GB
Q3_K_XL 110.69 GB file
116.84 GB
Q3_XXS 110.69 GB file
116.84 GB
Q4_XS 142.31 GB file
148.46 GB
Q4_NL 142.31 GB file
148.46 GB
Q4_K_XL 142.31 GB file
148.46 GB
Q4_K_S 142.31 GB file
148.46 GB
Q4_K_M Current
148.46 GB
Q4_1 142.31 GB file
148.46 GB
Q5_K_M 173.94 GB file
180.09 GB
Q5_K_S 173.94 GB file
180.09 GB
Q5_K_XL 173.94 GB file
180.09 GB
Q6_K 205.56 GB file
211.71 GB
Q6_K_XL 205.56 GB file
211.71 GB
Q1_M 253 GB file
259.15 GB
Q1_S 253 GB file
259.15 GB
Q8_0 268.81 GB file
274.96 GB
Q8_K_XL 268.81 GB file
274.96 GB
BF16 506 GB file
512.15 GB
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Frequently Asked Questions

Can I run Llama-3_1-Nemotron-Ultra-253B-v1-GGUF Q4_K_M on a consumer GPU?
Running Llama-3_1-Nemotron-Ultra-253B-v1-GGUF Q4_K_M locally requires 148.46 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_1-Nemotron-Ultra-253B-v1-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_K_M 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.