Antigma Q8_0 2505B Parameters

VRAM Requirements for
Devstral-Small-2505-GGUF Q8_0

To run Devstral-Small-2505-GGUF locally at Q8_0 quantization, you need at minimum 2704.6 GB of GPU VRAM.

2704.6 GB
Required VRAM
2661.56 GB
File Size
8K tokens
Context Window
2505B
Parameters
Estimated VRAM Required
2704.6
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 2,544.6 GB short
Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

⚠️ 2,544.6 GB short
Balanced $60,000+

2× H100 80GB NVLink

160 GB HBM3

NVLink bridge enables unified 160GB VRAM pool.

⚠️ 1,304.6 GB short
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) 2661.56 GB
Context Overhead (8,192 tokens × 2505B × 2 ÷ 1M) 41.042 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 2704.6 GB

Try a Different Quantization

Use the interactive calculator to compare Devstral-Small-2505-GGUF across all available formats.

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Devstral-Small-2505-GGUF — Other Quantizations

Q2_0 782.81 GB file
825.85 GB
Q3_0 1095.94 GB file
1138.98 GB
Q4_0 1409.06 GB file
1452.1 GB
Q5_0 1722.19 GB file
1765.23 GB
Q6_0 2035.31 GB file
2078.35 GB
Q8_0 Current
2704.6 GB
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Frequently Asked Questions

Can I run Devstral-Small-2505-GGUF Q8_0 on a consumer GPU?
Running Devstral-Small-2505-GGUF Q8_0 locally requires 2704.6 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 Devstral-Small-2505-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.