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License: MIT GitHub stars Status Downloads Version Repo Size Open Issues Open Pull Requests

This guide shows you how to build a cutting-edge AI server with 8x 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.

📚 Table of Contents


🏁 Introduction   🔝

This tutorial is for anyone aiming to build a high-performance AI server with 8 GPUs. Whether you're a researcher, developer, or enthusiast, you'll learn everything from hardware selection and assembly to system configuration and initial testing. Finish with a robust platform ready for demanding AI workloads.

🛠️ Preparation   🔝


🤖 Assembly   🔝

See detailed steps


⚙️ 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:

  • PCIe Settings Static Badge
    🚨📢🔔⚠️ Set all PCIe slots to the highest supported speed (Gen4/Gen5) and configure bifurcation for your GPUs.

    Advanced -> Chipset Configuration -> PCIE link width -> set MCIO2/1, MCIO4/3, MCIO6/5, MCIO8/7, MCIO12/11, MCIO14/13, MCIO16/15, MCIO18/17 to x16
    
  • Above 4G Decoding Static Badge
    🚨📢🔔⚠️ Enable "Above 4G Decoding" to address large GPU memory.

    May be enabled by default
    
  • Resizable BAR Static Badge
    🚨📢🔔⚠️ 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:

How-to-set-up-BIOS.mp4


🧪 Testing   🔝

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



Booting.mp4


📦 Bill of Materials (BOM)   🔝


📝 License   🔝

This project is open source under the MIT License.


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A hands-on guide for AI builders: make your own RTX 4090D/5090 GPU server that’s fast and efficient.

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