jerome
05eccd5b53
- Moved scattered scripts, templates, and documentation into organized directories (applications/, scripts/, assets/). - Updated .gitignore to strictly exclude secrets, state files, and IDE configs. - Added comprehensive README.md outlining repository structure and best practices. - Preserved all existing documentation and technical architecture files. - Prepared infrastructure/ for AWS Org and Proxmox Terraform management.
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05 - AWS Cloud Infrastructure — Home
Purpose: AWS architecture, networking, and cost optimization for AI workloads.
Overview
This section covers our AWS cloud infrastructure setup for scalable AI services, including:
- VPC networking architecture
- EC2 instance optimization for GPU workloads
- S3 data pipeline automation
- Cost optimization strategies
Infrastructure Components
AWS Architecture
- US-East-1 (N. Virginia): Primary production environment
- Multi-AZ deployment: High availability for critical services
- VPC networking: Isolated subnets for compute, storage, and monitoring
Compute Resources
- p4d.24xlarge: A100-based instances for training
- g5.xlarge: A10 instances for inference
- m5.large: CPU-based instances for web services
- Spot instances: Cost savings for non-critical workloads
Storage
- S3 buckets: Model weights, datasets, backup archives
- EBS volumes: High-performance storage for active VMs
- Glacier: Cold storage for historical data
Cost Optimization
- Reserved Instances: 1-3 year commitments for baseline workloads
- Spot Instances: 70% savings for fault-tolerant tasks
- S3 Lifecycle Policies: Automatic tiering to Glacier
- CloudWatch Alarms: Budget alerts and anomaly detection
Related Documents
- [Qwen3.5-MoE Setup](../02\ -\ TECHNICAL\ INFRASTRUCTURE/04\ -\ Qwen3.5\ Model\ Setup/) — Model deployment strategies
Our AWS infrastructure supports our SMB AI services and agent deployments with maximum efficiency.