autonomous-ai/autonomous-computer
Build your Personal AI Computer. Own your intelligence.
What it solves
This project provides open-source hardware blueprints and specifications for building high-performance local AI computers. It addresses the need for data privacy, independence from cloud API providers, and the elimination of recurring inference costs by enabling users to run large models (like Llama, Qwen, and DeepSeek) on their own physical hardware.
How it works
The repository provides detailed build guides, Bill of Materials (BOM), CAD files, and BIOS settings for four different hardware configurations based on the user's needs:
- Entry-level (2x RTX 5090): Designed for personal use and quantized models.
- Team build (4x RTX 5090): Designed for larger open models with low latency.
- On-prem business build (8x RTX 5090): Designed for developing, serving, and fine-tuning models in-house.
- Server build (4x RTX PRO 6000): A rack-ready 5U chassis for data centers with 384 GB of VRAM for the largest open models.
Once the hardware is assembled, the project suggests using local AI engines like vLLM, Ollama, or llama.cpp, or the Grid orchestrator to manage the local AI network.
Who it’s for
- Individuals wanting to run personal AI agents and stacks locally.
- Teams requiring private, low-latency AI infrastructure.
- Businesses that need to fine-tune and serve models without their data leaving their premises.
- Data center operators looking for rack-ready, open-source AI server specifications.
Highlights
- Open-source hardware: Includes every part, bracket, and BIOS setting needed for assembly.
- Scalable configurations: Offers builds ranging from 2 to 8 GPUs for varying VRAM and compute needs.
- End-to-end stack: Combines hardware blueprints with guidance on BIOS tuning, GPU testing, and software orchestration.
- Privacy-focused: Eliminates reliance on third-party cloud APIs and the risk of data being used for training.