This illustration contrasts an old approach of endlessly adding more GPU servers, burning money for little gain, with a new era where AI-driven optimization of software, network, cooling and power delivers smarter GPUs and a much better ROI.
Next AI
Tag: optimization
The optimization
This diagram illustrates the fundamental purpose and stages of optimization.
Basic Purpose of Optimization:
Optimization
- Core Principle: Perform only necessary actions
- Code Level: Remove unnecessary elements
Two Goals of Optimization:
1. More Speed
- O(n): Algorithm (Logic) improvement
- Techniques: Caching/Parallelization/Recursion optimization
2. Less Resource
- Memory: Reduce memory usage
- Management: Dynamic & Static memory optimization
Optimization Implementation Stages:
Stage 1: SW Level (Software Level)
- Code-level optimization
Stage 2: HW Implementation (Hardware Implementation)
- Offload heavy workloads to hardware
- Applied when software optimization is insufficient
Optimization Process:
Input → Processing → Output → Verification
- Deterministic INPUT Data: Structured input (DB Schema)
- Rule-based: Apply rule-based optimization
- Deterministic OUTPUT: Predictable results
- Verification: Validate speed, resource usage through benchmarking and profiling
Summary:
Optimization aims to increase speed and reduce resources by removing unnecessary operations. It follows a staged approach starting from software-level improvements and extending to hardware implementation when needed. The process ensures predictable, verifiable results through deterministic inputs/outputs and rule-based methods.
#Optimization #PerformanceTuning #CodeOptimization #AlgorithmImprovement #SoftwareEngineering #HardwareAcceleration #ResourceManagement #SpeedOptimization #MemoryOptimization #SystemDesign #Benchmarking #Profiling #EfficientCode #ComputerScience #SoftwareDevelopment
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Cooling with AI works
AI Workload Cooling Systems: Bidirectional Physical-Software Optimization
This image summarizes four cutting-edge research studies demonstrating the bidirectional optimization relationship between AI LLMs and cooling systems. It proves that physical cooling infrastructure and software workloads are deeply interconnected.
🔄 Core Concept of Bidirectional Optimization
Direction 1: Physical Cooling → AI Performance Impact
- Cooling methods directly affect LLM/VLM throughput and stability
Direction 2: AI Software → Cooling Control
- LLMs themselves act as intelligent controllers for cooling systems
📊 Research Analysis
1. Physical Cooling Impact on AI Performance (2025 arXiv)
[Cooling HW → AI SW Performance]
- Experiment: Liquid vs Air cooling comparison on H100 nodes
- Physical Differences:
- GPU Temperature: Liquid 41-50°C vs Air 54-72°C (up to 22°C difference)
- GPU Power Consumption: 148-173W reduction
- Node Power: ~1kW savings
- Software Performance Impact:
- Throughput: 54 vs 46 TFLOPs/GPU (+17% improvement)
- Sustained and predictable performance through reduced throttling
- Improved performance/watt (perf/W) ratio
→ Physical cooling improvements directly enhance AI workload real-time processing capabilities
2. AI Controls Cooling Systems (2025 arXiv)
[AI SW → Cooling HW Control]
- Method: Offline Reinforcement Learning (RL) for automated data center cooling control
- Results: 14-21% cooling energy reduction in 2000-hour real deployment
- Bidirectional Effects:
- AI algorithms optimally control physical cooling equipment (CRAC, pumps, etc.)
- Saved energy → enables more LLM job execution
- Secured more power headroom for AI computation expansion
→ AI software intelligently controls physical cooling to improve overall system efficiency
3. LLM as Cooling Controller (2025 OpenReview)
[AI SW ↔ Cooling HW Interaction]
- Innovative Approach: Using LLMs as interpretable controllers for liquid cooling systems
- Simulation Results:
- Temperature Stability: +10-18% improvement vs RL
- Energy Efficiency: +12-14% improvement
- Bidirectional Interaction Significance:
- LLMs interpret real-time physical sensor data (temperature, flow rate, etc.)
- Multi-objective trade-off optimization between cooling requirements and energy saving
- Interpretability: LLM decision-making process is human-understandable
- Result: Reduced throttling/interruptions → improved AI workload stability
→ Complete closed-loop where AI controls physical systems, and results feedback to AI performance
4. Physical Cooling Innovation Enables AI Training (E-Energy’25 PolyU)
[Cooling HW → AI SW Training Stability]
- Method: Immersion cooling applied to LLM training
- Physical Benefits:
- Dramatically reduced fan/CRAC overhead
- Lower PUE (Power Usage Effectiveness) achieved
- Uniform and stable heat removal
- Impact on AI Training:
- Enables stable long-duration training (eliminates thermal spikes)
- Quantitative power-delay trade-off optimization per workload
- Continuous training environment without interruptions
→ Advanced physical cooling technology secures feasibility of large-scale LLM training
🔁 Physical-Software Interdependency Map
┌─────────────────────────────────────────────────────────┐
│ Physical Cooling Systems │
│ (Liquid cooling, Immersion, CRAC, Heat exchangers) │
└──────────────┬────────────────────────┬─────────────────┘
↓ ↑
Temp↓ Power↓ Stability↑ AI-based Control
↓ RL/LLM Controllers
┌──────────────┴────────────────────────┴─────────────────┐
│ AI Workloads (LLM/VLM) │
│ Performance↑ Throughput↑ Throttling↓ Training Stability↑│
└───────────────────────────────────────────────────────────┘
💡 Key Insights: Bidirectional Optimization Synergy
1. Bottom-Up Influence (Physical → Software)
- Better cooling → maintains higher clock speeds/throughput
- Temperature stability → predictable performance, no training interruptions
- Power efficiency → enables simultaneous operation of more GPUs
2. Top-Down Influence (Software → Physical)
- AI algorithms provide real-time optimal control of cooling equipment
- LLM’s interpretable decision-making ensures operational transparency
- Adaptive cooling strategies based on workload characteristics
3. Virtuous Cycle Effect
Better cooling → AI performance improvement → smarter cooling control
→ Energy savings → more AI jobs → advanced cooling optimization
→ Sustainable large-scale AI infrastructure
🎯 Practical Implications
These studies demonstrate:
- Cooling is no longer passive infrastructure: It’s an active determinant of AI performance
- AI optimizes its own environment: Meta-level self-optimizing systems
- Hardware-software co-design is essential: Isolated optimization is suboptimal
- Simultaneous achievement of sustainability and performance: Synergy, not trade-off
📝 Summary
These four studies establish that next-generation AI data centers must evolve into integrated ecosystems where physical cooling and software workloads interact in real-time to self-optimize. The bidirectional relationship—where better cooling enables superior AI performance, and AI algorithms intelligently control cooling systems—creates a virtuous cycle that simultaneously achieves enhanced performance, energy efficiency, and sustainable scalability for large-scale AI infrastructure.
#EnergyEfficiency#GreenAI#SustainableAI#DataCenterOptimization#ReinforcementLearning#AIControl#SmartCooling
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Why/When Optimization ??
Analysis of Optimization Strategy Framework
Upper Graph: Stable Requirements Environment
- Characteristics: Predictable requirements with minimal fluctuation
- 100% Optimization Results:
- “Very Difficult” (high implementation cost)
- “No Efficiency” (poor ROI)
- Conclusion: Over-optimization is unnecessary in stable environments
Lower Graph: Volatile Requirements Environment
- Characteristics: Frequent requirement changes with high uncertainty
- Optimization Level Analysis:
- Peak Support (Blue): Reactive approach handling only maximum loads
- 60-80% Optimization (Green): “Easy & High Efficiency” ⭐
- 100% Optimization (Red): “Very Difficult” + “Still No Efficiency”
Key Insights
1. 60-80% Optimization as the Sweet Spot
- Easy to achieve with reasonable effort
- High efficiency in terms of cost-benefit ratio
- Realistic and practical range for most business contexts
2. Environment-Specific Optimization Strategy
Stable Environment → Minimal optimization sufficient
Volatile Environment → 60-80% optimization optimal
3. The 100% Optimization Trap
- Universally inefficient across all environments
- Very difficult to achieve with no efficiency gains
- Classic example of over-engineering
Practical Application Guide
60% Level: Minimum Professional Standard
- MVP releases
- Time-constrained projects
- Experimental features
70% Level: General Target
- Standard business products
- Most commercial services
- Typical quality benchmarks
80% Level: High-Quality Standard
- Core business functions
- Customer-facing critical services
- Brand-value related elements
Business Implementation Framework
For Stable Environments:
- Focus on basic functionality
- Avoid premature optimization
- Maintain simplicity
For Volatile Environments:
- Target 60-80% optimization range
- Prioritize adaptability over perfection
- Implement iterative improvements
Conclusion: Philosophy of Practical Optimization
This framework demonstrates that “good enough” often outperforms “perfect” in real-world scenarios. The 60-80% optimization zone represents the intersection of achievability, efficiency, and business value—particularly crucial in today’s rapidly changing business landscape. True optimization isn’t about reaching 100%; it’s about finding the right balance between effort invested and value delivered, while maintaining the agility to adapt when requirements inevitably change.
(!) 60-80% is just a number. The best number is changed by …
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Bitnet
BitNet Architecture Analysis
Overview
BitNet is an innovative neural network architecture that achieves extreme efficiency through ultra-low precision quantization while maintaining model performance through strategic design choices.
Key Features
1. Ultra-Low Precision (1.58-bit)
- Uses only 3 values: {-1, 0, +1} for weights
- Entropy calculation: log₂(3) ≈ 1.58 bits
- More efficient than standard 2-bit (4 values) representation
2. Weight Quantization
- Ternary weight system with correlation-based interpretation:
- +1: Positive correlation
- -1: Negative correlation
- 0: No relation
3. Multi-Layer Structure
- Leverages combinatorial power of multi-layer architecture
- Enables non-linear function approximation despite extreme quantization
4. Precision-Targeted Operations
- Minimizes high-precision operations
- Combines 8-bit activation (input data) with 1.58-bit weights
- Precise activation functions where needed
5. Hardware & Kernel Optimization
- CPU (ARM) kernel-level optimization
- Leverages bitwise operations (especially multiply → bit operations)
- Memory management through SIMD instructions
- Supports non-standard nature of 1.58-bit data
6. Token Relationship Computing
- Single token uses N weights of {1, -1, 0} to calculate relationships with all other tokens
Summary
BitNet represents a breakthrough in neural network efficiency by using extreme weight quantization (1.58-bit) that dramatically reduces memory usage and computational complexity while preserving model performance through hardware-optimized bitwise operations and multi-layer combinatorial representation power.
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Massive simple parallel computing
This diagram presents a systematic framework that defines the essence of AI LLMs as “Massive Simple Parallel Computing” and systematically outlines the resulting issues and challenges that need to be addressed.
Core Definition of AI LLM: “Massive Simple Parallel Computing”
Massive: Enormous scale with billions of parameters Simple: Fundamentally simple computational operations (matrix multiplications, etc.) Parallel: Architecture capable of simultaneous parallel processing Computing: All of this implemented through computational processes
Core Issues Arising from This Essential Nature
Big Issues:
- Black-box unexplainable: Incomprehensibility due to massive and complex interactions
- Energy-intensive: Enormous energy consumption inevitably arising from massive parallel computing
Essential Requirements Therefore Needed
Very Required:
- Verification: Methods to ensure reliability of results given the black-box characteristics
- Optimization: Approaches to simultaneously improve energy efficiency and performance
The Ultimate Question: “By What?”
How can we solve all these requirements?
In other words, this framework poses the fundamental question about specific solutions and approaches to overcome the problems inherent in the essential characteristics of current LLMs. This represents a compressed framework showing the core challenges for next-generation AI technology development.
The diagram effectively illustrates how the defining characteristics of LLMs directly lead to significant challenges, which in turn demand specific capabilities, ultimately raising the critical question of implementation methodology.
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3 Key on the AI era
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:
- AI infra (AI Infrastructure) – The foundational technology that powers AI systems
- AI Model – Core algorithms and model technologies represented by neural networks
- 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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Lechuck Park 