How much VRAM do I need for Marco-DeepResearch-8B-GGUF Q3_K_M?

Yes! At 5.63 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-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_K_M quality good enough for production?

Q3_K_M is suitable for specialized use cases. Check community benchmarks for specific quality metrics.

VRAM Requirements for Marco-DeepResearch-8B-GGUF Q3_K_M

Recommended GPU Configurations

RTX 4060 (8GB)

RTX 4070 (12GB)

RTX 4090 (24GB)

📊 VRAM Calculation Breakdown

Try a Different Quantization

Marco-DeepResearch-8B-GGUF — Other Quantizations

Frequently Asked Questions

VRAM Requirements for
Marco-DeepResearch-8B-GGUF Q3_K_M