Darque Dante Q6_0 70B Parameters

VRAM Requirements for
LLama-3-Mega-Dolphin-2.9.1-120b_GGUF Q6_0

To run LLama-3-Mega-Dolphin-2.9.1-120b_GGUF locally at Q6_0 quantization, you need at minimum 60.03 GB of GPU VRAM.

60.03 GB
Required VRAM
56.88 GB
File Size
8K tokens
Context Window
70B
Parameters
Estimated VRAM Required
60.03
GB
Data Centre Class
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 12.0 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_0) 56.88 GB
Context Overhead (8,192 tokens × 70B × 2 ÷ 1M) 1.147 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 60.03 GB

Try a Different Quantization

Use the interactive calculator to compare LLama-3-Mega-Dolphin-2.9.1-120b_GGUF across all available formats.

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LLama-3-Mega-Dolphin-2.9.1-120b_GGUF — Other Quantizations

Q6_0 Current
60.03 GB
FP16 140 GB file
143.15 GB
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Frequently Asked Questions

Can I run LLama-3-Mega-Dolphin-2.9.1-120b_GGUF Q6_0 on a consumer GPU?
Running LLama-3-Mega-Dolphin-2.9.1-120b_GGUF Q6_0 locally requires 60.03 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-Mega-Dolphin-2.9.1-120b_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_0 quality good enough for production?
Q6_0 is suitable for specialized use cases. Check community benchmarks for specific quality metrics.