Brittlewis12 Q5_K_S 2507B Parameters

VRAM Requirements for
Devstral-Small-2507-GGUF Q5_K_S

To run Devstral-Small-2507-GGUF locally at Q5_K_S quantization, you need at minimum 1766.63 GB of GPU VRAM.

1766.63 GB
Required VRAM
1723.56 GB
File Size
8K tokens
Context Window
2507B
Parameters
Estimated VRAM Required
1766.63
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 1,606.6 GB short
Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

⚠️ 1,606.6 GB short
Balanced $60,000+

2× H100 80GB NVLink

160 GB HBM3

NVLink bridge enables unified 160GB VRAM pool.

⚠️ 366.6 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 (Q5_K_S) 1723.56 GB
Context Overhead (8,192 tokens × 2507B × 2 ÷ 1M) 41.075 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 1766.63 GB

Try a Different Quantization

Use the interactive calculator to compare Devstral-Small-2507-GGUF across all available formats.

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Devstral-Small-2507-GGUF — Other Quantizations

Q2_K 783.44 GB file
826.51 GB
Q2_K_S 783.44 GB file
826.51 GB
Q2_M 783.44 GB file
826.51 GB
Q2_S 783.44 GB file
826.51 GB
Q2_XS 783.44 GB file
826.51 GB
Q2_XXS 783.44 GB file
826.51 GB
Q3_K_L 1096.81 GB file
1139.88 GB
Q3_K_M 1096.81 GB file
1139.88 GB
Q3_K_S 1096.81 GB file
1139.88 GB
Q3_M 1096.81 GB file
1139.88 GB
Q3_S 1096.81 GB file
1139.88 GB
Q3_XS 1096.81 GB file
1139.88 GB
Q3_XXS 1096.81 GB file
1139.88 GB
Q4_K_M 1410.19 GB file
1453.26 GB
Q4_XS 1410.19 GB file
1453.26 GB
Q4_K_S 1410.19 GB file
1453.26 GB
Q4_NL 1410.19 GB file
1453.26 GB
Q4_1 1410.19 GB file
1453.26 GB
Q4_0 1410.19 GB file
1453.26 GB
Q5_K_S Current
1766.63 GB
Q5_1 1723.56 GB file
1766.63 GB
Q5_K_M 1723.56 GB file
1766.63 GB
Q5_0 1723.56 GB file
1766.63 GB
Q6_K 2036.94 GB file
2080.01 GB
Q1_S 2507 GB file
2550.07 GB
Q1_M 2507 GB file
2550.07 GB
Q8_0 2663.69 GB file
2706.76 GB
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Frequently Asked Questions

Can I run Devstral-Small-2507-GGUF Q5_K_S on a consumer GPU?
Running Devstral-Small-2507-GGUF Q5_K_S locally requires 1766.63 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-2507-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 Q5_K_S quality good enough for production?
Q5_K_S is suitable for specialized use cases. Check community benchmarks for specific quality metrics.