A hardware-focused tutorial on building a dedicated AI inference server using consumer components. Focus on the sweet spot of dual used RTX 3090s or a single RTX 4090.

Key Sections:
1. **Component Selection:** Why VRAM is king. The concept of 'VRAM per dollar'.
2. **The Build:** Physical assembly notes, cooling requirements for continuous load.
3. **BIOS & OS Configuration:** PCIe bifurcation, Ubuntu Server optimizations, NVIDIA driver headless setup.
4. **Model Partitioning:** Using tensor parallelism to split 70B+ models across consumer cards.
5. **Cost vs Cloud:** ROI calculation showing break-even point against GPT-4 API costs.

**Internal Linking Strategy:** Link back to Pillar. Link natively to 'Deploying Local LLMs to Kubernetes' for next steps.

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A detailed hardware and software guide for building a ~$1,500 dedicated AI inference server capable of running DeepSeek-R1 70B locally. Covers two GPU configurations (dual RTX 3090 vs single RTX 4090), component selection using a VRAM-per-dollar framework, physical assembly and thermal planning, BIOS/Ubuntu Server setup, NVIDIA headless driver installation, deploying DeepSeek-R1 with vLLM tensor parallelism across two GPUs, nginx reverse proxy configuration for network access, and a detailed ROI break-even analysis comparing local hosting against GPT-4 and DeepSeek cloud API costs.

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The Physical Build: Assembly and Thermal Planning

Deploying DeepSeek-R1 with Tensor Parallelism