A I D C A I Q1_S 8B Parameters

VRAM Requirements for
Marco-DeepResearch-8B-i1-GGUF Q1_S

To run Marco-DeepResearch-8B-i1-GGUF locally at Q1_S quantization, you need at minimum 10.13 GB of GPU VRAM.

10.13 GB

Required VRAM

8 GB

File Size

8K tokens

Context Window

8B

Parameters

Estimated VRAM Required

10.13

GB

Mid-Range GPU Required

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

Recommended GPU Configurations

⚠️ 0.1 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 (Q1_S) 8 GB

Context Overhead (8,192 tokens × 8B × 2 ÷ 1M) 0.131 GB

System Buffer (OS + CUDA runtime) 2.00 GB

Total Required VRAM 10.13 GB

Try a Different Quantization

Use the interactive calculator to compare Marco-DeepResearch-8B-i1-GGUF across all available formats.

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Marco-DeepResearch-8B-i1-GGUF — Other Quantizations

Q2_K 2.5 GB file

Q2_K_S 2.5 GB file

Q2_M 2.5 GB file

Q2_S 2.5 GB file

Q2_XS 2.5 GB file

Q2_XXS 2.5 GB file

Q3_K_L 3.5 GB file

Q3_K_M 3.5 GB file

Q3_K_S 3.5 GB file

Q3_M 3.5 GB file

Q3_S 3.5 GB file

Q3_XS 3.5 GB file

Q3_XXS 3.5 GB file

Q4_NL 4.5 GB file

Q4_K_S 4.5 GB file

Q4_K_M 4.5 GB file

Q4_1 4.5 GB file

Q4_0 4.5 GB file

Q4_XS 4.5 GB file

Q5_K_M 5.5 GB file

Q5_K_S 5.5 GB file

Q6_K 6.5 GB file

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Frequently Asked Questions

Can I run Marco-DeepResearch-8B-i1-GGUF Q1_S on a consumer GPU?

Yes! At 10.13 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 Marco-DeepResearch-8B-i1-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 Q1_S quality good enough for production?

Q1_S is suitable for specialized use cases. Check community benchmarks for specific quality metrics.