Tomngdev Q3_K_L 139B Parameters

VRAM Requirements for
MiniMax-M2.5-REAP-139B-A10B-GGUF Q3_K_L

To run MiniMax-M2.5-REAP-139B-A10B-GGUF locally at Q3_K_L quantization, you need at minimum 65.09 GB of GPU VRAM.

65.09 GB
Required VRAM
60.81 GB
File Size
8K tokens
Context Window
139B
Parameters
Estimated VRAM Required
65.09
GB
Data Centre Class
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 17.1 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 (Q3_K_L) 60.81 GB
Context Overhead (8,192 tokens × 139B × 2 ÷ 1M) 2.277 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 65.09 GB

Try a Different Quantization

Use the interactive calculator to compare MiniMax-M2.5-REAP-139B-A10B-GGUF across all available formats.

Open Live Calculator →

MiniMax-M2.5-REAP-139B-A10B-GGUF — Other Quantizations

Q2_K 43.44 GB file
47.72 GB
Q3_K_L Current
65.09 GB
Q3_K_M 60.81 GB file
65.09 GB
Q3_K_S 60.81 GB file
65.09 GB
Q4_K_M 78.19 GB file
82.47 GB
Q4_K_S 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
FP4 139 GB file
143.28 GB
Q8_0 147.69 GB file
151.97 GB
BF16 278 GB file
282.28 GB
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Frequently Asked Questions

Can I run MiniMax-M2.5-REAP-139B-A10B-GGUF Q3_K_L on a consumer GPU?
Running MiniMax-M2.5-REAP-139B-A10B-GGUF Q3_K_L locally requires 65.09 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 MiniMax-M2.5-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 Q3_K_L quality good enough for production?
Q3_K_L is suitable for specialized use cases. Check community benchmarks for specific quality metrics.