Bartowski Q8_0 162B Parameters

VRAM Requirements for
cerebras_MiniMax-M2-REAP-162B-A10B-GGUF Q8_0

To run cerebras_MiniMax-M2-REAP-162B-A10B-GGUF locally at Q8_0 quantization, you need at minimum 176.78 GB of GPU VRAM.

176.78 GB
Required VRAM
172.13 GB
File Size
8K tokens
Context Window
162B
Parameters
Estimated VRAM Required
176.78
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 16.8 GB short
Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

⚠️ 16.8 GB short
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 (Q8_0) 172.13 GB
Context Overhead (8,192 tokens × 162B × 2 ÷ 1M) 2.654 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 176.78 GB

Try a Different Quantization

Use the interactive calculator to compare cerebras_MiniMax-M2-REAP-162B-A10B-GGUF across all available formats.

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cerebras_MiniMax-M2-REAP-162B-A10B-GGUF — Other Quantizations

Q2_K 50.63 GB file
55.28 GB
Q2_K_L 50.63 GB file
55.28 GB
Q2_M 50.63 GB file
55.28 GB
Q2_S 50.63 GB file
55.28 GB
Q2_XS 50.63 GB file
55.28 GB
Q2_XXS 50.63 GB file
55.28 GB
Q3_K_L 70.88 GB file
75.53 GB
Q3_K_M 70.88 GB file
75.53 GB
Q3_K_S 70.88 GB file
75.53 GB
Q3_K_XL 70.88 GB file
75.53 GB
Q3_M 70.88 GB file
75.53 GB
Q3_XS 70.88 GB file
75.53 GB
Q3_XXS 70.88 GB file
75.53 GB
Q4_K_M 91.13 GB file
95.78 GB
Q4_XS 91.13 GB file
95.78 GB
Q4_K_S 91.13 GB file
95.78 GB
Q4_NL 91.13 GB file
95.78 GB
Q4_1 91.13 GB file
95.78 GB
Q4_0 91.13 GB file
95.78 GB
Q5_K_M 111.38 GB file
116.03 GB
Q5_K_S 111.38 GB file
116.03 GB
Q6_K 131.63 GB file
136.28 GB
Q1_S 162 GB file
166.65 GB
Q1_M 162 GB file
166.65 GB
Q8_0 Current
176.78 GB
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Frequently Asked Questions

Can I run cerebras_MiniMax-M2-REAP-162B-A10B-GGUF Q8_0 on a consumer GPU?
Running cerebras_MiniMax-M2-REAP-162B-A10B-GGUF Q8_0 locally requires 176.78 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-162B-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 Q8_0 quality good enough for production?
Q8_0 produces near-lossless quality compared to FP16. It's widely used in production deployments where quality is critical and you can afford the extra VRAM.