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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The $1,500 Local AI Server: DeepSeek-R1 on Consumer Hardware
on SitePoint.
