LLM’s Computing-Power-Cooling Relationship

This diagram illustrates the technical architecture and potential issues that can occur when operating LLMs (Large Language Models).

Normal Operation (Top Left)

1. Computing Requires – LLM workload is delivered to the processor
2. Power Requires – Power supplied via DVFS (Dynamic Voltage and Frequency Scaling)
3. Heat Generated – Heat is produced during computing processes
4. Cooling Requires – Temperature management through proper cooling systems

Problem Scenarios

Power Issue (Top Right)

• Symptom: Insufficient power (kW & Quality)
• Results:
  • Computing performance degradation
  • Power throttling or errors
  • LLM workload errors

Cooling Issue (Bottom Right)

• Symptom: Insufficient cooling (Temperature & Density)
• Results:
  • Abnormal heat generation
  • Thermal throttling or errors
  • Computing performance degradation
  • LLM workload errors

Key Message

For stable LLM operations, the three elements of Computing-Power-Cooling must be balanced. If any one element is insufficient, it leads to system-wide performance degradation or errors. This emphasizes that AI infrastructure design must consider not only computing power but also adequate power supply and cooling systems together.

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Summary

• LLM operation requires a critical balance between computing, power supply, and cooling infrastructure.
• Insufficient power causes power throttling, while inadequate cooling leads to thermal throttling, both resulting in workload errors.
• Successful AI infrastructure design must holistically address all three components rather than focusing solely on computational capacity.

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WIth Claude