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
Primary Loop: The cooling tower releases heat to the outside air, while the chiller produces chilled water that is supplied to the CDU
Secondary Loop: Coolant distributed from the CDU flows through the Row Manifold to each server rack’s Rack Manifold, cooling the servers
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.
This diagram illustrates a server room thermal management system workflow.
System Architecture
Server Internal Components:
AI Workload, GPU Workload, and Power Workload are connected to the CPU, generating heat
Temperature Monitoring Points:
Supply Temp: Cold air supplied from the cooling system
CoolZone Temp: Temperature in the cooling zone
Inlet Temp: Server inlet temperature
Outlet Temp: Server outlet temperature
Hot Zone Temp: Temperature in the heat exhaust zone
Return Temp : Hot air return to the cooling system
Cooling System:
The Cooling Workload on the left manages overall cooling
Closed-loop cooling system that circulates back via Return Temp
Temperature Delta Monitoring
The bottom flowchart shows how each workload affects temperature changes (ΔT):
Delta temperature sensors (Δ1, Δ2, Δ3) measure temperature differences across each section
This data enables analysis of each workload’s thermal impact and optimization of cooling efficiency
This system appears to be a data center thermal management solution designed to effectively handle high heat loads from AI and GPU-intensive workloads. The comprehensive temperature monitoring allows for precise control and optimization of the cooling infrastructure based on real-time workload demands.
Power Consumption Rise: 0.42kW → 1.26kW (3x increase)
Temperature Delta Rise: 7°C → 17°C (increased heat generation)
Cooling System Response:
Water flow rate: 200 LPM → 600 LPM (3x increase)
Fan speed: 600 RPM → 1200 RPM (2x increase)
Operational Prediction Implications
Operating Costs: Approximately 3x increase from baseline expected
Spare Capacity: 40% cooling system capacity remaining
Expansion Capability: Current setup can accommodate additional 67% GPU load
This AI data center monitoring dashboard illustrates the cascading resource changes when GPU workload increases from 30% to 90%, triggering proportional increases in power consumption (3x), cooling flow rate (3x), and fan speed (2x). The system demonstrates predictable operational scaling patterns, with current cooling capacity showing 40% remaining headroom for additional GPU load expansion. Note: All values are estimated figures for demonstration purposes.
Note: All numerical values are estimated figures for demonstration purposes and do not represent actual measured data.
This image illustrates a data monitoring system for an AI data center server room. Titled “Data in AI DC Server Room,” it depicts the relationships between key elements being monitored in the data center.
The system consists of four main components, each with detailed metrics:
GPU Workload – Right center
Computing Load: GPU utilization rate (%) and type of computational tasks (training vs. inference)
Power Consumption: Real-time power consumption of each GPU – Example: NVIDIA H100 GPU consumes up to 700W
Workload Pattern: Periodicity of workload (peak/off-peak times) and predictability
Power Usage: Real-time power output and efficiency of UPS, PDU, and transformers
Power Quality: Voltage, frequency stability, and power loss rate
Power Capacity: Types and proportions of supplied energy, ensuring sufficient power availability for current workload operations
Cooling System – Right
Cooling Device Status: Air-cooling fan speed (RPM), liquid cooling pump flow rate (LPM), and coolant temperature (°C)
Environmental Conditions: Data center internal temperature, humidity, air pressure, and hot/cold zone temperatures – critical for server operations
Cooling Efficiency: Power Usage Effectiveness (PUE) and proportion of power consumed by the cooling system
Server/Rack – Top center
Rack Power Density: Power consumption per rack (kW) – Example: GPU server racks range from 30 to 120 kW
Temperature Profile: Temperature (°C) of GPUs, CPUs, memory modules, and heat distribution
Server Status: Operational state of servers (active/standby) and workload distribution status
The workflow sequence indicated at the bottom of the diagram represents:
① GPU WORK: Initial execution of AI workloads – GPU computational tasks begin, generating system load
② with POWER USE: Increased power supply for GPU operations – Power demand increases with GPU workload, and power infrastructure responds accordingly
③ COOLING WORK: Cooling processes activated in response to heat generation
Sensing: Temperature sensors detect server and rack thermal conditions, monitoring hot/cold zone temperature differentials
Analysis: Analysis of collected temperature data, determining cooling requirements
Action: Adjustment of cooling equipment (fan speed, coolant flow rate, etc. automatically regulated)
④ SERVER OK: Maintenance of normal server operation through proper power supply and cooling – Temperature and power remain stable, allowing GPU workloads to continue running under optimal conditions
The arrows indicate data flow and interrelationships between systems, showing connections from power infrastructure to servers and from cooling systems to servers. This integrated system enables efficient and stable data center operation by detecting increased power demand and heat generation from GPU workloads, and adjusting cooling systems in real-time accordingly.
This image is a diagram showing the key components of a Data Center (DC).
The diagram visually represents the core elements that make up a data center:
Building – Shown on the left with a building icon, representing the physical structure of the data center.
Core infrastructure elements (in the central blue area):
Network – Data communication infrastructure
Computing – Servers and processing equipment
Power – Energy supply systems
Cooling – Temperature regulation systems
The central orange circle represents server racks, which is connected to power supply units (transformers), cooling equipment, and network devices.
Digital Service – Displayed on the right, representing the end services that all this infrastructure ultimately delivers.
This diagram illustrates how a data center flows from a physical building through core elements like network, computing, power, and cooling to ultimately provide digital services.
This diagram titled “Data is Connected in AI DC” illustrates the relationships starting from workload scheduling in an AI data center.
Key aspects of the diagram:
The entire system’s interconnected relationships begin with workload scheduling.
The diagram divides the process into two major phases:
Deterministic phase: Primarily concerned with power requirements that operate in a predictable, planned manner.
Statistical phase: Focused on cooling requirements, where predictions vary based on external environmental conditions.
The “Prophet Commander” at the workload scheduling stage can predict/direct future requirements, allowing the system to prepare power (1.1 Power Ready!!) and cooling (1.2 Cooling Ready!!) in advance.
Process flow:
Job allocation from workload scheduling to GPU cluster
GPUs request and receive power
Temperature rises due to operations
Cooling system detects temperature and activates cooling
This diagram illustrates the interconnected workflow in AI data centers, beginning with workload scheduling that enables predictive resource management. The process flows from deterministic power requirements to statistical cooling needs, with the “Prophet Commander” enabling proactive preparation of power and cooling resources. This integrated approach demonstrates how workload prediction can drive efficient resource allocation throughout the entire AI data center ecosystem.
This image titled “Data Explosion in Data Center” illustrates three key challenges faced by modern data centers:
Data/Computing:
Shows the explosive growth of data from computing servers to internet/cloud infrastructure and AI technologies.
Visualizes the exponential increase in data volume from 1X to 100X, 10,000X, and ultimately to 1,000,000,000X (one billion times).
Depicts how servers, computers, mobile devices, and global networks connect to massive data nodes, generating and processing enormous amounts of information.
Power:
Addresses the increasing power supply requirements needed to support the data explosion in data centers.
Shows various energy sources including traditional power plants, wind turbines, solar panels, and battery storage systems to meet the growing energy demands.
Represents energy efficiency and sustainable power supply through a cyclical system indicated by green arrows.
Cooling:
Illustrates the heat management challenges resulting from increased data processing and their solutions.
Explains the shift from traditional air cooling methods to more efficient server liquid cooling technologies.
Visualizes modern cooling solutions with blue circular arrows representing the cooling cycle.
This diagram comprehensively explains how the exponential growth of data impacts data center design and operations, particularly highlighting the challenges and innovations in power consumption and thermal management.
– Example: NVIDIA H100 GPU consumes up to 700W
