How much VRAM do I need for dolphin-2.9.1-mixtral-1x22b-IMat-GGUF Q3_XXS?

You need 11.99 GB of VRAM to run dolphin-2.9.1-mixtral-1x22b-IMat-GGUF at Q3_XXS quantization. The model file is 9.63 GB with a context window of 8,192 tokens.

Legraphista Q3_XXS 22B Parameters

VRAM Requirements for
dolphin-2.9.1-mixtral-1x22b-IMat-GGUF Q3_XXS

To run dolphin-2.9.1-mixtral-1x22b-IMat-GGUF locally at Q3_XXS quantization, you need at minimum 11.99 GB of GPU VRAM.

11.99 GB

Required VRAM

9.63 GB

File Size

8K tokens

Context Window

22B

Parameters

Estimated VRAM Required

11.99

Mid-Range GPU Required

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

Recommended GPU Configurations

⚠️ 2.0 GB short

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 (Q3_XXS) 9.63 GB

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

System Buffer (OS + CUDA runtime) 2.00 GB

Total Required VRAM 11.99 GB

Try a Different Quantization

Use the interactive calculator to compare dolphin-2.9.1-mixtral-1x22b-IMat-GGUF across all available formats.

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dolphin-2.9.1-mixtral-1x22b-IMat-GGUF — Other Quantizations

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Frequently Asked Questions

Can I run dolphin-2.9.1-mixtral-1x22b-IMat-GGUF Q3_XXS on a consumer GPU?

Yes! At 11.99 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 dolphin-2.9.1-mixtral-1x22b-IMat-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 Q3_XXS quality good enough for production?

Q3_XXS is suitable for specialized use cases. Check community benchmarks for specific quality metrics.

VRAM Requirements for dolphin-2.9.1-mixtral-1x22b-IMat-GGUF Q3_XXS

Recommended GPU Configurations

RTX 3080 (10GB)

RTX 4070 Ti (12GB)

RTX 4090 (24GB)

📊 VRAM Calculation Breakdown

Try a Different Quantization

dolphin-2.9.1-mixtral-1x22b-IMat-GGUF — Other Quantizations

Frequently Asked Questions

VRAM Requirements for
dolphin-2.9.1-mixtral-1x22b-IMat-GGUF Q3_XXS