Yasu Oh Q4_XS 2506B Parameters

VRAM Requirements for
Magistral-Small-2506-GGUF Q4_XS

To run Magistral-Small-2506-GGUF locally at Q4_XS quantization, you need at minimum 1452.69 GB of GPU VRAM.

1452.69 GB
Required VRAM
1409.63 GB
File Size
8K tokens
Context Window
2506B
Parameters
Estimated VRAM Required
1452.69
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 1,292.7 GB short
Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

⚠️ 1,292.7 GB short
Balanced $60,000+

2× H100 80GB NVLink

160 GB HBM3

NVLink bridge enables unified 160GB VRAM pool.

⚠️ 52.7 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 (Q4_XS) 1409.63 GB
Context Overhead (8,192 tokens × 2506B × 2 ÷ 1M) 41.058 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 1452.69 GB

Try a Different Quantization

Use the interactive calculator to compare Magistral-Small-2506-GGUF across all available formats.

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Magistral-Small-2506-GGUF — Other Quantizations

Q4_K_M 1409.63 GB file
1452.69 GB
Q4_XS Current
1452.69 GB
Q5_K_M 1722.88 GB file
1765.94 GB
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Frequently Asked Questions

Can I run Magistral-Small-2506-GGUF Q4_XS on a consumer GPU?
Running Magistral-Small-2506-GGUF Q4_XS locally requires 1452.69 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 Magistral-Small-2506-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 Q4_XS quality good enough for production?
Q4_K_M is an excellent balance of quality and performance. Perplexity tests show minimal degradation (< 2%) vs FP16 for most models. Suitable for most production applications.