LMM Operation

Posted on by lechuck park

LLM Operations System Analysis

This diagram illustrates the architecture of an LLM Operations (LLMOps) system, demonstrating how Large Language Models are deployed and operated in industrial settings.

Key Components and Data Flow

1. Data Input Sources (3 Categories)

  • Facility: Digitized sensor data that gets detected and generates alert/event logs
  • Manual: Equipment manuals and technical documentation
  • Experience: Operational manuals including SOP/MOP/EOP (Standard/Maintenance/Emergency Operating Procedures)

2. Central Processing System

  • RAG (Retrieval-Augmented Generation): A central hub that integrates and processes all incoming data
  • Facility data is visualized through metrics and charts for monitoring purposes

3. LLM Operations

  • The central LLM synthesizes all information to provide intelligent operational support
  • Interactive interface enables user communication and queries

4. Final Output and Control

  • Dashboard for data visualization and monitoring
  • AI chatbot for real-time operational assistance
  • Operator Control: The bottom section shows checkmark (✓) and X-mark (✗) buttons along with an operator icon, indicating that final decision-making authority remains with human operators

System Characteristics

This system represents a smart factory solution that integrates AI into traditional industrial operations, providing comprehensive management from real-time data monitoring to operational manual utilization.

The key principle is that while AI provides comprehensive analysis and recommendations, the final operational decisions and approvals still rest with human operators. This is clearly represented through the operator icon and approval/rejection buttons at the bottom of the diagram.

This demonstrates a realistic and desirable AI operational model that emphasizes safety, accountability, and the importance of human judgment in unpredictable situations.

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3 Key on the AI era

Posted on by lechuck park

This diagram illustrates the 3 Core Technological Components of AI World and their surrounding challenges.

AI World’s 3 Core Technological Components

Central AI World Components:

  1. AI infra (AI Infrastructure) – The foundational technology that powers AI systems
  2. AI Model – Core algorithms and model technologies represented by neural networks
  3. AI Agent – Intelligent systems that perform actual tasks and operations

Surrounding 3 Key Challenges

1. Data – Left Area

Data management as the raw material for AI technology:

  • Data: Raw data collection
  • Verified: Validated and quality-controlled data
  • Easy to AI: Data preprocessed and optimized for AI processing

2. Optimization – Bottom Area

Performance enhancement of AI technology:

  • Optimization: System optimization
  • Fit to data: Data fitting and adaptation
  • Energy cost: Efficiency and resource management

3. Verification – Right Area

Ensuring reliability and trustworthiness of AI technology:

  • Verification: Technology validation process
  • Right?: Accuracy assessment
  • Humanism: Alignment with human-centered values

This diagram demonstrates how the three core technological elements – AI Infrastructure, AI Model, and AI Agent – form the center of AI World, while interacting with the three fundamental challenges of Data, Optimization, and Verification to create a comprehensive AI ecosystem.

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network issue in a GPU workload

Posted on by lechuck park

This diagram illustrates network bottleneck issues in large-scale AI/ML systems.

Key Components:

Left side:

  • Big Data and AI Model/Workload connected to the system via network

Center:

  • Large-scale GPU cluster (multiple GPUs arranged in a grid pattern)
  • Each GPU is interconnected for distributed processing

Right side:

  • Power supply and cooling systems

Core Problem:

The network interface specifications shown at the bottom reveal bandwidth mismatches:

  • inter GPU NVLink: 600GB/s
  • inter Server Infiniband: 400Gbps
  • CPU/RAM/DISK PCIe/NVLink: (relatively lower bandwidth)

“One Issue” – System-wide Propagation:

A network bottleneck or failure at a specific point (marked with red circle) “spreads throughout the entire system” as indicated by the yellow arrows.

This diagram warns that in large-scale AI training, a single network bottleneck can have catastrophic effects on overall system performance. It visualizes how bandwidth imbalances at various levels – GPU-to-GPU communication, server-to-server communication, and storage access – can compromise the efficiency of the entire system. The cascading effect demonstrates how network issues can quickly propagate and impact the performance of distributed AI workloads across the infrastructure.

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Data Center Mgt. System Req.

Posted on by lechuck park

System Components (Top Level)

Six core components:

  • Facility: Data center physical infrastructure
  • Data List: Data management and cataloging
  • Data Converter: Data format conversion
  • Network: Network infrastructure
  • Server: Server hardware
  • Software (Database): Applications and database systems

Universal Mandatory Requirements

Fundamental requirements applied to ALL components:

  • Stability (24/7 HA): 24/7 High Availability – All systems must operate continuously without interruption
  • Performance: Optimal performance assurance – All components must meet required performance levels

Component-Specific Additional Requirements

1. Data List

  • Sampling Rate, Computing Power, HW/SW Interface

2. Data Converter

  • Data Capacity, Computing Power, Program Logic (control facilities), High Availability

3. Network

  • Private NW, Bandwidth, Architecture (L2/L3, Ring/Star), UTP/Optic, Management Include

4. Server

  • Computing Power, Storage Sizing, High Availability, External (Public Network)

5. Software/Database

  • Data Integrity, Cloud-like High Availability & Scale-out, Monitoring, Event Management, Analysis (AI)

This architecture emphasizes that stability and performance are fundamental prerequisites for data center operations, with each component having its own specific additional requirements built upon these two essential foundation requirements.

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Parallel Processing

Posted on by lechuck park

Parallel Processing System Analysis

System Architecture

1. Input Stage – Independent Processing

  • Multiple tasks are simultaneously input into the system in parallel
  • Each task can be processed independently of others

2. Central Processing Network

Blue Nodes (Modification Work)

  • Processing units that perform actual data modifications or computations
  • Handle parallel incoming tasks simultaneously

Yellow Nodes (Propagation Work)

  • Responsible for propagating changes to other nodes
  • Handle system-wide state synchronization

3. Synchronization Stage

  • Objective: “Work & Wait To Make Same State”
  • Wait until all nodes reach identical state
  • Essential process for ensuring data consistency

Performance Characteristics

Advantage: Massive Parallel

  • Increased throughput through large-scale parallel processing
  • Reduced overall processing time by executing multiple tasks simultaneously

Disadvantage: Massive Wait Cost

  • Wait time overhead for synchronization
  • Entire system must wait for the slowest node
  • Performance degradation due to synchronization overhead

Key Trade-off

Parallel processing systems must balance performance enhancement with data consistency:

  • More parallelism = Higher performance, but more complex synchronization
  • Strong consistency guarantee = Longer wait times, but stable data state

This concept is directly related to the CAP Theorem (Consistency, Availability, Partition tolerance), which is a fundamental consideration in distributed system design.

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