Green Eyed Devil Q3_K_M 106B Parameters

VRAM Requirements for
Monika-106B-GGUFs Q3_K_M

To run Monika-106B-GGUFs locally at Q3_K_M quantization, you need at minimum 50.12 GB of GPU VRAM.

50.12 GB
Required VRAM
46.38 GB
File Size
8K tokens
Context Window
106B
Parameters
Estimated VRAM Required
50.12
GB
Data Centre Class
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 2.1 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 (Q3_K_M) 46.38 GB
Context Overhead (8,192 tokens × 106B × 2 ÷ 1M) 1.737 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 50.12 GB

Try a Different Quantization

Use the interactive calculator to compare Monika-106B-GGUFs across all available formats.

Open Live Calculator →

Monika-106B-GGUFs — Other Quantizations

Q2_0 33.13 GB file
36.87 GB
Q3_K_M Current
50.12 GB
Q4_K_M 59.63 GB file
63.37 GB
Q5_K_M 72.88 GB file
76.62 GB
Q6_K 86.13 GB file
89.87 GB
Q1_0 106 GB file
109.74 GB
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Frequently Asked Questions

Can I run Monika-106B-GGUFs Q3_K_M on a consumer GPU?
Running Monika-106B-GGUFs Q3_K_M locally requires 50.12 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 Monika-106B-GGUFs?
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 Q3_K_M quality good enough for production?
Q3_K_M is suitable for specialized use cases. Check community benchmarks for specific quality metrics.