per Watt with AI

This image titled “per Watt with AI” is a diagram explaining the paradigm shift in power efficiency following the AI era, particularly after the emergence of LLMs.

Overall Context

Core Structure of AI Development:

  • Machine Learning = Computing = Using Power
  • The equal signs (=) indicate that these three elements are essentially the same concept. In other words, AI machine learning inherently means large-scale computing, which inevitably involves power consumption.

Characteristics of LLMs: As AI, particularly LLMs, have proven their effectiveness, tremendous progress has been made. However, due to their technical characteristics, they have the following structure:

  • Huge Computing: Massively parallel processing of simple tasks
  • Huge Power: Enormous power consumption due to this parallel processing
  • Huge Cost: Power costs and infrastructure expenses

Importance of Power Efficiency Metrics

With hardware advancements making this approach practically effective, power consumption has become a critical issue affecting even the global ecosystem. Therefore, power is now used as a performance indicator for all operations.

Key Power Efficiency Metrics

Performance-related:

  • FLOPs/Watt: Floating-point operations per watt
  • Inferences/Watt: Number of inferences processed per watt
  • Training/Watt: Training performance per watt

Operations-related:

  • Workload/Watt: Workload processing capacity per watt
  • Data/Watt: Data processing capacity per watt
  • IT Work/Watt: IT work processing capacity per watt

Infrastructure-related:

  • Cooling/Watt: Cooling efficiency per watt
  • Water/Watt: Water usage efficiency per watt

This diagram illustrates that in the AI era, power efficiency has become the core criterion for all performance evaluations, transcending simple technical metrics to encompass environmental, economic, and social perspectives.

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Learning , Reasoning, Inference

This image illustrates the three core processes of AI LLMs by drawing parallels to human learning and cognitive processes.

Learning

  • Depicted as a wise elderly scholar reading books in a library
  • Represents the lifelong process of absorbing knowledge and experiences accumulated by humanity over generations
  • The bottom icons show data accumulation and knowledge storage processes
  • Meaning: Just as AI learns human language and knowledge through vast text data, humans also build knowledge throughout their lives through continuous learning and experience

Reasoning

  • Shows a character deep in thought, surrounded by mathematical formulas
  • Represents the complex mental process of confronting a problem and searching for solutions through internal contemplation
  • The bottom icons symbolize problem analysis and processing stages
  • Meaning: The human cognitive process of using learned knowledge to engage in logical thinking and analysis to solve problems

Inference

  • Features a character confidently exclaiming “THE ANSWER IS CLEAR!”
  • Expresses the confidence and decisiveness when finally finding an answer after complex thought processes
  • The bottom checkmark signifies reaching a final conclusion
  • Meaning: The human act of ultimately speaking an answer or making a behavioral decision through thought and analysis

These three stages visually demonstrate how AI processes information in a manner similar to the natural human sequence of learning → thinking → conclusion, connecting AI’s technical processes to familiar human cognitive patterns.

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TCS (Technology Cooling Loop)

This image shows a diagram of the TCS (Technology Cooling Loop) system structure.

System Components

The First Loop:

  • Cooling Tower: Dissipates heat to the atmosphere
  • Chiller: Generates chilled water
  • CDU (Coolant Distribution Unit): Distributes coolant throughout the system

The Second Main Loop:

  • Row Manifold: Distributes cooling water to each server rack row
  • Rack Manifold: Individual rack-level cooling water distribution system
  • Server Racks: IT equipment racks that require cooling

System Operation

  1. Primary Loop: The cooling tower releases heat to the outside air, while the chiller produces chilled water that is supplied to the CDU
  2. Secondary Loop: Coolant distributed from the CDU flows through the Row Manifold to each server rack’s Rack Manifold, cooling the servers
  3. Circulation System: The heated coolant returns to the CDU where it is re-cooled through the primary loop

This is an efficient cooling system used in data centers and large-scale IT facilities. It systematically removes heat generated by server equipment to ensure stable operations through a two-loop architecture that separates the heat rejection process from the precision cooling delivery to IT equipment.

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1210th day

Eeumee.net: Your Daily Dose of Tech Clarity!

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  • Visual Learning, Daily: We create a new, concise diagram (like a PPT page) every single day, breaking down essential technical elements into clear, visual summaries.
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Imaged by ChatGPT, Promotion text by Gemini

HIRING AN AI AGENT

In the future, organizations might hire AI agents instead of people, shifting focus from salaries to model usage fees, performance, and data quality. Choosing the right AI could require strong problem definition and benchmarking, along with infrastructure like data centers and computing power.

with ChatGPT

Data Center Digitalization

This image presents a roadmap for “Data Center Digitalization” showing the evolutionary process. Based on your explanation, here’s a more accurate interpretation:

Top 4 Core Concepts (Purpose for All Stages)

  • Check Point: Current state inspection and verification point for each stage
  • Respond to change: Rapid response system to quick changes
  • Target Image: Final target state to be achieved
  • Direction: Overall strategic direction setting

Digital Transformation Evolution Stages

Stage 1: Experience-Based Digital Environment Foundation

  • Easy to Use: Creating user-friendly digital environments through experience
  • Integrate Experience: Integrating existing data center operational experience and know-how into the digital environment
  • Purpose: Utilizing existing operational experience as checkpoints to establish a foundation for responding to changes

Stage 2: DevOps Integrated Environment Configuration

  • DevOps: Development-operations integrated environment supporting Fast Upgrade
  • Building efficient development-operations integrated systems based on existing operational experience and know-how
  • Purpose: Implementing DevOps environment that can rapidly respond to changes based on integrated experience

Stage 3: Evolution to Intelligent Digital Environment

  • Digital Twin & AI Agent(LLM): Accumulated operational experience and know-how evolve into digital twins and AI agents
  • Intelligent automated decision-making through Operation Evolutions
  • Purpose: Establishing intelligent systems toward the target image and confirming operational direction

Stage 4: Complete Automation Environment Achievement

  • Robotics: Unmanned operations through physical automation
  • Digital 99.99% Automation: Nearly complete digital automation environment integrating all experience and know-how
  • Purpose: Achieving the final target image – complete digital environment where all experience is implemented as automation

Final Goal: Simultaneous Development of Stability and Efficiency

WIN-WIN Achievement:

  • Stable: Ensuring high availability and reliability based on accumulated operational experience
  • Efficient: Maximizing operational efficiency utilizing integrated know-how

This diagram presents a strategic roadmap where data centers systematically integrate existing operational experience and know-how into digital environments, evolving step by step while reflecting the top 4 core concepts as purposes for each stage, ultimately achieving both stability and efficiency simultaneously.

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By Charts

This image visually explains various ways charts help in decision-making.

Here’s a breakdown of the key elements:

Left Side:

  • An icon representing a chart is shown. This signifies the role of charts in visually representing data.

Center:

Five main roles of charts in contributing to decision-making are listed:

  1. Detecting Short-Term Anomalies (Problem Identification): Charts help in identifying short-term unusual patterns and pinpointing problems.
  2. Analyzing Long-Term Trends (Future Planning & Identifying Savings Opportunities): Charts are used to understand long-term data tendencies, which aids in future planning and discovering cost-saving opportunities.
  3. Comparing Against Baselines (Performance Measurement & Benchmarking): Charts are utilized to measure current performance against predefined baselines and for benchmarking purposes.
  4. Identifying Savings Opportunities: Through chart analysis, areas or methods for cost reduction can be identified.
  5. Communicating Insights Effectively (Stakeholder Reporting & Decision Making): Charts are valuable for visualizing complex data in an easy-to-understand manner, assisting in stakeholder reporting and supporting decision-making.

Right Side:

  • An icon depicting people connected by arrows is visible, with the text “Help for Decisions.” This indicates that all the roles of charts mentioned above ultimately aim to facilitate effective decision-making.

In summary, this image emphasizes that charts go beyond simple data visualization; they are essential tools for identifying problems, understanding trends, measuring performance, discovering opportunities, and ultimately leading to clear decision-making through data analysis.

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