Anthracite Org Q6_K 72B Parameters

VRAM Requirements for
magnum-v2-72b-gguf Q6_K

To run magnum-v2-72b-gguf locally at Q6_K quantization, you need at minimum 61.68 GB of GPU VRAM.

61.68 GB
Required VRAM
58.5 GB
File Size
8K tokens
Context Window
72B
Parameters
Estimated VRAM Required
61.68
GB
Data Centre Class
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 13.7 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 (Q6_K) 58.5 GB
Context Overhead (8,192 tokens × 72B × 2 ÷ 1M) 1.18 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 61.68 GB

Try a Different Quantization

Use the interactive calculator to compare magnum-v2-72b-gguf across all available formats.

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magnum-v2-72b-gguf — Other Quantizations

Q3_M 31.5 GB file
34.68 GB
Q3_S 31.5 GB file
34.68 GB
Q4_K_M 40.5 GB file
43.68 GB
Q4_XS 40.5 GB file
43.68 GB
Q5_K_M 49.5 GB file
52.68 GB
Q6_K Current
61.68 GB
Q8_0 76.5 GB file
79.68 GB
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Frequently Asked Questions

Can I run magnum-v2-72b-gguf Q6_K on a consumer GPU?
Running magnum-v2-72b-gguf Q6_K locally requires 61.68 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 magnum-v2-72b-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 Q6_K quality good enough for production?
Q6_K is suitable for specialized use cases. Check community benchmarks for specific quality metrics.