How much VRAM do I need for Mistral-Small-Instruct-2409-22B-NEO-Imatrix-GGUF Q2_XS?

You need 9.24 GB of VRAM to run Mistral-Small-Instruct-2409-22B-NEO-Imatrix-GGUF at Q2_XS quantization. The model file is 6.88 GB with a context window of 8,192 tokens.

David A U Q2_XS 22B Parameters

VRAM Requirements for
Mistral-Small-Instruct-2409-22B-NEO-Imatrix-GGUF Q2_XS

To run Mistral-Small-Instruct-2409-22B-NEO-Imatrix-GGUF locally at Q2_XS quantization, you need at minimum 9.24 GB of GPU VRAM.

9.24 GB

Required VRAM

6.88 GB

File Size

8K tokens

Context Window

22B

Parameters

Estimated VRAM Required

9.24

Mid-Range GPU Required

0 8GB
RTX 3060 16GB
RTX 3080 24GB
RTX 4090 48GB
A6000 80GB
A100 80GB+

Recommended GPU Configurations

Budget $350 – $500

RTX 3080 (10GB)

10 GB

Used market gem. Tight on VRAM but viable for this workload.

Balanced $699 – $799

RTX 4070 Ti (12GB)

12 GB

Strong inference GPU. Handles 7-13B models comfortably.

Ultimate $1,599 – $1,999

RTX 4090 (24GB)

24 GB

Best consumer GPU. Breeze through 13B models at any quantization.

📊 VRAM Calculation Breakdown

Model File Size (Q2_XS) 6.88 GB

Context Overhead (8,192 tokens × 22B × 2 ÷ 1M) 0.36 GB

System Buffer (OS + CUDA runtime) 2.00 GB

Total Required VRAM 9.24 GB

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Use the interactive calculator to compare Mistral-Small-Instruct-2409-22B-NEO-Imatrix-GGUF across all available formats.

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Mistral-Small-Instruct-2409-22B-NEO-Imatrix-GGUF — Other Quantizations

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Frequently Asked Questions

Can I run Mistral-Small-Instruct-2409-22B-NEO-Imatrix-GGUF Q2_XS on a consumer GPU?

Yes! At 9.24 GB VRAM required, a single high-end consumer GPU like the RTX 4090 (24GB) can handle this workload. You can also use multiple GPUs for tensor parallelism.

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 Mistral-Small-Instruct-2409-22B-NEO-Imatrix-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 Q2_XS quality good enough for production?

Q2_XS is suitable for specialized use cases. Check community benchmarks for specific quality metrics.

VRAM Requirements for Mistral-Small-Instruct-2409-22B-NEO-Imatrix-GGUF Q2_XS

Recommended GPU Configurations

RTX 3080 (10GB)

RTX 4070 Ti (12GB)

RTX 4090 (24GB)

📊 VRAM Calculation Breakdown

Try a Different Quantization

Mistral-Small-Instruct-2409-22B-NEO-Imatrix-GGUF — Other Quantizations

Frequently Asked Questions

VRAM Requirements for
Mistral-Small-Instruct-2409-22B-NEO-Imatrix-GGUF Q2_XS