Bartowski Q3_K_L 172B Parameters

VRAM Requirements for
cerebras_MiniMax-M2-REAP-172B-A10B-GGUF Q3_K_L

To run cerebras_MiniMax-M2-REAP-172B-A10B-GGUF locally at Q3_K_L quantization, you need at minimum 80.07 GB of GPU VRAM.

80.07 GB
Required VRAM
75.25 GB
File Size
8K tokens
Context Window
172B
Parameters
Estimated VRAM Required
80.07
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

Balanced $60,000+

2× H100 80GB NVLink

160 GB HBM3

NVLink bridge enables unified 160GB VRAM pool.

Ultimate $3,000,000+

NVIDIA GB200 NVL72

1.4 TB HBM3e

Next-gen Grace Blackwell Superchip. Built for frontier models.

📊 VRAM Calculation Breakdown

Model File Size (Q3_K_L) 75.25 GB
Context Overhead (8,192 tokens × 172B × 2 ÷ 1M) 2.818 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 80.07 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 53.75 GB file
58.57 GB
Q2_K_L 53.75 GB file
58.57 GB
Q2_M 53.75 GB file
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_L Current
80.07 GB
Q3_K_M 75.25 GB file
80.07 GB
Q3_K_S 75.25 GB file
80.07 GB
Q3_K_XL 75.25 GB file
80.07 GB
Q3_M 75.25 GB file
80.07 GB
Q3_XS 75.25 GB file
80.07 GB
Q3_XXS 75.25 GB file
80.07 GB
Q4_K_M 96.75 GB file
101.57 GB
Q4_XS 96.75 GB file
101.57 GB
Q4_K_S 96.75 GB file
101.57 GB
Q4_NL 96.75 GB file
101.57 GB
Q4_1 96.75 GB file
101.57 GB
Q4_0 96.75 GB file
101.57 GB
Q5_K_M 118.25 GB file
123.07 GB
Q5_K_S 118.25 GB file
123.07 GB
Q6_K 139.75 GB file
144.57 GB
Q1_S 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 Q3_K_L on a consumer GPU?
Running cerebras_MiniMax-M2-REAP-172B-A10B-GGUF Q3_K_L locally requires 80.07 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 Q3_K_L quality good enough for production?
Q3_K_L is suitable for specialized use cases. Check community benchmarks for specific quality metrics.