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This guide shows you how to build a cutting-edge AI server with 2x GPUs. From hardware selection to software setup, follow each step to create a high-performance platform for deep learning, data science, and GPU-intensive workloads.

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📚 Table of Contents

• 🏁 Introduction
• 🛠️ Preparation
• 🤖 Assembly
• ⚙️ Setup
• 🧪 Testing
• 📦 BOM
• 📝 License

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🏁 Introduction   🔝

This tutorial guides you through building a high-performance AI server equipped with two GPUs. Whether you're a researcher, developer, or enthusiast, you'll learn everything from selecting and assembling the right hardware to configuring your system and performing initial tests. By the end, you'll have a powerful platform ready for demanding AI workloads.

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🛠️ Preparation   🔝

1. Electronic & Electrical

2. Mechanical & Housing

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🤖 Assembly   🔝

See detailed steps

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⚙️ Setup   🔝

BIOS Optimization for GPU Performance

Tip: The default BIOS settings may not deliver optimal performance for multi-GPU workloads. Adjust these parameters for best results:

• Above 4G Decoding
  🚨📢🔔⚠️ Enable "Above 4G Decoding" to address large GPU memory.
  

  Advanced -> PCI Subsystems Settings -> Enable Above 4G Decoding
  

• Resizable BAR
  🚨📢🔔⚠️ Activate "Resizable BAR" for improved CPU-GPU data transfer.
  

  Advanced -> PCI Subsystems Settings -> Enable Re-size BAR support
  

• Power Management
  Disable unnecessary power-saving features (C-states, ASPM) that may throttle GPU performance.
  Optional

• Memory Configuration
  Set RAM to rated speed and enable XMP/DOCP profiles for max bandwidth.
  Optional

• Fan and Thermal Controls
  Adjust fan curves and thermal limits for optimal cooling.
  Optional

After saving changes, reboot and monitor GPU performance and stability.

References:

• Motherboard User Manual 💻🖱️
• BMC Documents 🤖

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🧪 Testing   🔝

Boot with WinPE from USB to verify hardware, or install Linux, NVIDIA drivers, and check with nvidia-smi or nvtop. Once confirmed, install your OS and start your AI work.

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📦 Bill of Materials (BOM)   🔝

• 𝄜 Bill of Materials

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📝 License   🔝

This project is open source under the MIT License.

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