Research Overview
This research collection explores different approaches to setting up self-hosted AI environments on AWS EC2 instances. These guides provide detailed instructions for deploying models like Llama 3, Mistral, DeepSeek, and others on cost-effective GPU instances, enabling powerful AI capabilities without recurring SaaS subscription fees.Research Collection
Comparative Analysis
| Platform | Recommended Instance | Monthly Cost (8h/day) | Best For |
|---|---|---|---|
| Gemini | g5.xlarge | ~$86 (Spot) | Development environment with code-server integration |
| Flowith | g5.4xlarge | ~$262 | Production environment for larger models (up to 30B) |
| DeepSeek | g4dn.xlarge | ~$205 | Specialized DeepSeek coder models |
| Grok | g6.xlarge | ~$193-201 | Latest generation GPU performance (NVIDIA L4) |
| Manus | g4dn.xlarge or g5.xlarge | ~$75-205 | Comprehensive deployment with security focus |
Key Insights
All approaches demonstrate significant cost savings compared to subscription AI services, especially when implementing auto-shutdown features during idle periods. Using spot instances can further reduce costs by 60-70%. The g4dn.xlarge offers good value for smaller models, while g5.xlarge/g5.4xlarge provide better performance for larger models or higher throughput requirements.
Best Practices
Implementing Nginx with SSL certificates ensures secure access, while auto-shutdown scripts during off-hours minimize costs. Docker containerization simplifies deployment and maintenance across different server configurations.
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