Tomngdev BF16 139B Parameters

VRAM Requirements for
MiniMax-M2.5-REAP-139B-A10B-GGUF BF16

To run MiniMax-M2.5-REAP-139B-A10B-GGUF locally at BF16 quantization, you need at minimum 282.28 GB of GPU VRAM.

282.28 GB
Required VRAM
278 GB
File Size
8K tokens
Context Window
139B
Parameters
Estimated VRAM Required
282.28
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 122.3 GB short
Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

⚠️ 122.3 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 (BF16) 278 GB
Context Overhead (8,192 tokens × 139B × 2 ÷ 1M) 2.277 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 282.28 GB

Try a Different Quantization

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

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

Q2_K 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
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 Current
282.28 GB
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Frequently Asked Questions

Can I run MiniMax-M2.5-REAP-139B-A10B-GGUF BF16 on a consumer GPU?
Running MiniMax-M2.5-REAP-139B-A10B-GGUF BF16 locally requires 282.28 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 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 BF16 quality good enough for production?
FP16/BF16 is the standard precision used for production inference and serves as the quality baseline. All fine-tuned models are typically served at this precision.