Dev Quasar Q2_K 172B Parameters

VRAM Requirements for
cerebras.MiniMax-M2-REAP-172B-A10B-GGUF Q2_K

To run cerebras.MiniMax-M2-REAP-172B-A10B-GGUF locally at Q2_K quantization, you need at minimum 58.57 GB of GPU VRAM.

58.57 GB
Required VRAM
53.75 GB
File Size
8K tokens
Context Window
172B
Parameters
Estimated VRAM Required
58.57
GB
Data Centre Class
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 10.6 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 (Q2_K) 53.75 GB
Context Overhead (8,192 tokens × 172B × 2 ÷ 1M) 2.818 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 58.57 GB

Try a Different Quantization

Use the interactive calculator to compare cerebras.MiniMax-M2-REAP-172B-A10B-GGUF across all available formats.

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cerebras.MiniMax-M2-REAP-172B-A10B-GGUF — Other Quantizations

Q2_K Current
58.57 GB
Q2_S 53.75 GB file
58.57 GB
Q2_XS 53.75 GB file
58.57 GB
Q2_XXS 53.75 GB file
58.57 GB
Q3_K_M 75.25 GB file
80.07 GB
Q4_K_M 96.75 GB file
101.57 GB
Q5_K_M 118.25 GB file
123.07 GB
Q6_K 139.75 GB file
144.57 GB
FP4 172 GB file
176.82 GB
Q1_M 172 GB file
176.82 GB
Q8_0 182.75 GB file
187.57 GB
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Frequently Asked Questions

Can I run cerebras.MiniMax-M2-REAP-172B-A10B-GGUF Q2_K on a consumer GPU?
Running cerebras.MiniMax-M2-REAP-172B-A10B-GGUF Q2_K locally requires 58.57 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 cerebras.MiniMax-M2-REAP-172B-A10B-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 Q2_K quality good enough for production?
Q2_K is suitable for specialized use cases. Check community benchmarks for specific quality metrics.