Bartowski Q1_S 139B Parameters

VRAM Requirements for
cerebras_MiniMax-M2-REAP-139B-A10B-GGUF Q1_S

To run cerebras_MiniMax-M2-REAP-139B-A10B-GGUF locally at Q1_S quantization, you need at minimum 143.28 GB of GPU VRAM.

143.28 GB
Required VRAM
139 GB
File Size
8K tokens
Context Window
139B
Parameters
Estimated VRAM Required
143.28
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 (Q1_S) 139 GB
Context Overhead (8,192 tokens × 139B × 2 ÷ 1M) 2.277 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 143.28 GB

Try a Different Quantization

Use the interactive calculator to compare cerebras_MiniMax-M2-REAP-139B-A10B-GGUF across all available formats.

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cerebras_MiniMax-M2-REAP-139B-A10B-GGUF — Other Quantizations

Q2_K 43.44 GB file
47.72 GB
Q2_K_L 43.44 GB file
47.72 GB
Q2_M 43.44 GB file
47.72 GB
Q2_S 43.44 GB file
47.72 GB
Q2_XS 43.44 GB file
47.72 GB
Q2_XXS 43.44 GB file
47.72 GB
Q3_K_L 60.81 GB file
65.09 GB
Q3_K_M 60.81 GB file
65.09 GB
Q3_K_S 60.81 GB file
65.09 GB
Q3_K_XL 60.81 GB file
65.09 GB
Q3_M 60.81 GB file
65.09 GB
Q3_XS 60.81 GB file
65.09 GB
Q3_XXS 60.81 GB file
65.09 GB
Q4_K_M 78.19 GB file
82.47 GB
Q4_XS 78.19 GB file
82.47 GB
Q4_K_S 78.19 GB file
82.47 GB
Q4_NL 78.19 GB file
82.47 GB
Q4_1 78.19 GB file
82.47 GB
Q4_0 78.19 GB file
82.47 GB
Q5_K_M 95.56 GB file
99.84 GB
Q5_K_S 95.56 GB file
99.84 GB
Q6_K 112.94 GB file
117.22 GB
Q1_S Current
143.28 GB
Q1_M 139 GB file
143.28 GB
Q8_0 147.69 GB file
151.97 GB
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Frequently Asked Questions

Can I run cerebras_MiniMax-M2-REAP-139B-A10B-GGUF Q1_S on a consumer GPU?
Running cerebras_MiniMax-M2-REAP-139B-A10B-GGUF Q1_S locally requires 143.28 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-139B-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 Q1_S quality good enough for production?
Q1_S is suitable for specialized use cases. Check community benchmarks for specific quality metrics.