Files

T

jerome 05eccd5b53 chore: consolidate Syslog Solution code into unified repository structure

- 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.

2026-05-07 11:40:02 +00:00

1.5 KiB

Raw Blame History

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

[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.

1.5 KiB Raw Blame History