Power Control : UPS vs ESS

Posted on by lechuck park

ESS System Analysis for AI Datacenter Power Control

This diagram illustrates the ESS (Energy Storage System) technology essential for providing flexible high-power supply for AI datacenters. Goldman Sachs Research forecasts that AI will drive a 165% increase in datacenter power demand by 2030, with AI representing about 19% of datacenter power demand by 2028, necessitating advanced power management beyond traditional UPS limitations.

ESS System Features for AI Datacenter Applications

1. High Power Density Battery System

  • Rapid Charge/Discharge: Immediate response to sudden power fluctuations in AI workloads
  • Large-Scale Storage: Massive power backup capacity for GPU-intensive AI processing
  • High Power Density: Optimized for space-constrained datacenter environments

2. Intelligent Power Management Capabilities

  • Overload Management: Handles instantaneous high-power demands during AI inference/training
  • GPU Load Prediction: Analyzes AI model execution patterns to forecast power requirements
  • High Response Speed: Millisecond-level power injection/conversion preventing AI processing interruptions
  • Predictive Analytics: Machine learning-based power demand forecasting

3. Flexible Operation Optimization

  • Peak Shaving: Reduces power costs during AI workload peak hours
  • Load Balancing: Distributes power loads across multiple AI model executions
  • Renewable Energy Integration: Supports sustainable AI datacenter operations
  • Cost Optimization: Minimizes AI operational expenses through intelligent power management

Central Power Management System – Essential Core Component of ESS

The Central Power Management System is not merely an auxiliary feature but a critical essential component of ESS for AI datacenters:

1. Precise Data Collection

  • Real-time monitoring of power consumption patterns by AI workload type
  • Tracking power usage across GPU, CPU, memory, and other components
  • Integration of environmental conditions and cooling system power data
  • Comprehensive telemetry from all datacenter infrastructure elements

2. AI-Based Predictive Analysis

  • Machine learning algorithms for AI workload prediction
  • Power demand pattern learning and optimization
  • Predictive maintenance for failure prevention
  • Dynamic resource allocation based on anticipated needs

3. Fast Automated Logic

  • Real-time automated power distribution control
  • Priority-based power allocation during emergency situations
  • Coordinated control across multiple ESS systems
  • Autonomous decision-making for optimal power efficiency

ESS Advantages over UPS for AI Datacenter Applications

While traditional UPS systems are limited to simple backup power during outages, ESS is specifically designed for the complex and dynamic power requirements of AI datacenters:

Proactive vs. Reactive

  • UPS: Reactive response to power failures
  • ESS: Proactive management of power demands before issues occur

Intelligence Integration

  • UPS: Basic power switching functionality
  • ESS: AI-driven predictive analytics and automated optimization

Scalability and Flexibility

  • UPS: Fixed capacity backup power
  • ESS: Dynamic scaling to handle AI servers that use up to 10 times the power of standard servers

Operational Optimization

  • UPS: Emergency power supply only
  • ESS: Continuous power optimization, cost reduction, and efficiency improvement

This advanced ESS approach is critical as datacenter capacity has grown 50-60% quarter over quarter since Q1 2023, requiring sophisticated power management solutions that can adapt to the unprecedented energy demands of modern AI infrastructure.

Future-Ready Infrastructure

ESS represents the evolution from traditional backup power to intelligent energy management, essential for supporting the next generation of AI datacenters that demand both reliability and efficiency at massive scale.

With Cluade

Evolutions and THE NEXT?

Posted on by lechuck park

This illustration depicts the evolution of human-machine interaction in four stages:

  1. Manual Tools – A human uses basic tools, representing traditional manual labor.
  2. Machine Operation – A worker operates a mechanical machine, indicating the industrial age.
  3. Programmed Automation – A robotic system with a CPU chip functions automatically based on human-developed programs.
  4. AI Collaboration – An AI-powered robot with a GPU chip works interactively with a human, showcasing the era of intelligent collaboration.

This is from “https://eeumee.net/2025/05/28/machine-changes/“

New Expert with AI

Posted on by lechuck park

Diagram Overview

This diagram illustrates the structural transformation of the professional services market in the AI era.

Current Situation (Left Side)

Users pay for three levels of professional services:

  • A+ Expert: Top-tier expertise and specialized knowledge
  • Expert: Mid-level professional services
  • Agent: Basic professional task handling

AI Era Transformation (Right Side)

Market Polarization:

  • A+ Expert Retained: “keep” – Highest-level human expertise remains essential
  • Mid-tier Replacement: “Replace” – Expert and Agent roles substituted by AI systems
  • Cost Concentration: Payment structure shifts from 3 categories → 2 categories

Key Implications

  1. Economic Efficiency: Reduced costs for mid-tier professional services
  2. Market Polarization: Premium human experts vs. AI systems structure
  3. Enhanced Accessibility: Democratization of professional services through AI
  4. Structural Transformation: Fundamental reshaping of professional service industries

Economic Impact

  • Winners: A+ Experts (strengthened monopolistic position), AI service providers, general consumers
  • Disrupted: Mid-tier professionals (Expert and Agent levels)
  • Market Change: Structural reorganization and pricing transformation in professional services

Conclusion

This diagram effectively demonstrates not just job displacement, but the economic restructuring of professional service markets, showing how AI-driven substitution leads to cost structure changes and market bipolarization.

With Claude

Human in the loop : HITP

Posted on by lechuck park

Human-in-the-Loop (HITL) System Diagram Analysis

Image Interpretation

Upper Section – Basic HITL Structure:

  • A three-stage process: Sensing → Analysis → Decision
  • Clear distinction between Machine Role and Human Role
  • Classified as a “Small Automation Step”

Lower Section – Real-world Complex Process:

  • Multiple HITL steps connected in a workflow pattern
  • Human intervention points (red “S” markers) at each stage labeled “It Also Human Role (Decision)”
  • Forms an overall “Real Big Process”
  • Complex structure with cyclical feedback loops

Key Concepts

This diagram illustrates not simple linear automation, but a collaborative system where humans and machines work together. It demonstrates that human judgment and intervention are required at each stage, and these small units combine to form larger business processes.

With Claude

Computing Changes with Power/Cooling

Posted on by lechuck park

This chart compares power consumption and cooling requirements for server-grade computing hardware.

CPU Servers (Intel Xeon, AMD EPYC)

  • 1U-4U Rack: 0.2-1.2kW power consumption
  • 208V power supply
  • Standard air cooling (CRAC, server fans) sufficient
  • PUE: 1.4-1.6 (Power Usage Effectiveness)

GPU Servers (DGX Series)

Power consumption and cooling complexity increase dramatically:

Low-Power Models (DGX-1, DGX-2)

  • 3.5-10kW power consumption
  • Tesla V100 GPUs
  • High-performance air cooling required

Medium-Power Models (DGX A100, H100)

  • 6.5-10.2kW power consumption
  • 400V high voltage required
  • Liquid cooling recommended or essential

Highest-Performance Models (DGX B200, GB200)

  • 14.3-120kW extreme power consumption
  • Blackwell architecture GPUs
  • Full liquid cooling essential
  • PUE 1.1-1.2 with improved cooling efficiency

Key Trends Summary

The evolution from CPU to GPU computing represents a fundamental shift in data center infrastructure requirements. Power consumption scales dramatically from kilowatts to tens of kilowatts, driving the transition from traditional air cooling to sophisticated liquid cooling systems. Higher-performance systems paradoxically achieve better power efficiency through advanced cooling technologies, while requiring substantial infrastructure upgrades including high-voltage power delivery and comprehensive thermal management solutions.

※ Disclaimer: All figures presented in this chart are approximate reference values and may vary significantly depending on actual environmental conditions, workloads, configurations, ambient temperature, and other operational factors.

WIth Claude