Dev Quasar Q2_XS 139B Parameters

VRAM Requirements for
cerebras.MiniMax-M2-REAP-139B-A10B-GGUF Q2_XS

To run cerebras.MiniMax-M2-REAP-139B-A10B-GGUF locally at Q2_XS quantization, you need at minimum 47.72 GB of GPU VRAM.

47.72 GB
Required VRAM
43.44 GB
File Size
8K tokens
Context Window
139B
Parameters
Estimated VRAM Required
47.72
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.

⚠️ 7.7 GB short
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_XS) 43.44 GB
Context Overhead (8,192 tokens × 139B × 2 ÷ 1M) 2.277 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 47.72 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_S 43.44 GB file
47.72 GB
Q2_XS Current
47.72 GB
Q2_XXS 43.44 GB file
47.72 GB
Q3_K_M 60.81 GB file
65.09 GB
Q4_K_M 78.19 GB file
82.47 GB
Q5_K_M 95.56 GB file
99.84 GB
Q6_K 112.94 GB file
117.22 GB
FP4 139 GB file
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 Q2_XS on a consumer GPU?
Running cerebras.MiniMax-M2-REAP-139B-A10B-GGUF Q2_XS locally requires 47.72 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 Q2_XS quality good enough for production?
Q2_XS is suitable for specialized use cases. Check community benchmarks for specific quality metrics.