Green Eyed Devil Q2_0 106B Parameters

VRAM Requirements for
Monika-106B-GGUFs Q2_0

To run Monika-106B-GGUFs locally at Q2_0 quantization, you need at minimum 36.87 GB of GPU VRAM.

36.87 GB
Required VRAM
33.13 GB
File Size
8K tokens
Context Window
106B
Parameters
Estimated VRAM Required
36.87
GB
Prosumer / Workstation
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $1,200 – $1,800

2× RTX 3090 (48GB total)

48 GB

Dual-GPU via tensor parallelism. Best cost per GB at this tier.

Balanced $4,000 – $5,500

NVIDIA A6000 (48GB)

48 GB

Single-card 48GB pro GPU. Clean setup, no multi-GPU overhead.

Ultimate $8,000 – $12,000

NVIDIA A100 40GB SXM

40 GB HBM2e

Data-centre HBM2e bandwidth. Dramatically faster throughput.

📊 VRAM Calculation Breakdown

Model File Size (Q2_0) 33.13 GB
Context Overhead (8,192 tokens × 106B × 2 ÷ 1M) 1.737 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 36.87 GB

Try a Different Quantization

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

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Monika-106B-GGUFs — Other Quantizations

Q2_0 Current
36.87 GB
Q3_K_M 46.38 GB file
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 Q2_0 on a consumer GPU?
Running Monika-106B-GGUFs Q2_0 locally requires 36.87 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 Q2_0 quality good enough for production?
Q2_0 is suitable for specialized use cases. Check community benchmarks for specific quality metrics.