& Immersion Cooling
Overview of Data Center Cooling Needs
Modern data centers generate significant amounts of heat due to the high density of computing hardware. Efficient cooling is essential to maintain the reliability and longevity of IT equipment while minimizing energy consumption and operating costs.
Over time, various cooling technologies have been developed, each with different approaches to thermal management. The most common methods include traditional air cooling, direct-to-chip liquid cooling, and immersion cooling.
Traditional
Air Cooling
Air cooling has been the predominant cooling method in data centers for decades. It relies on chilled air circulation to remove heat from servers and other IT hardware. The key components of an air-cooled data center include:
Computer Room Air Conditioners (CRACs) or Computer Room Air Handlers (CRAHs): These systems generate chilled air, often using refrigeration cycles or chilled water from an external chiller.
Raised Floors and Hot/Cold Aisle Containment: Raised floors allow chilled air to be distributed beneath the servers, while hot/cold aisle containment strategies optimize airflow efficiency by isolating hot and cold air streams.
Fans and Heat Sinks: Servers include internal fans and heat sinks to transfer heat from processors and memory to the surrounding air.
Limitations of Air Cooling
Inefficiency at high densities: As server density increases, air cooling becomes less effective due to thermal limitations and airflow constraints.
High energy consumption: Large fans, air conditioning units, and chillers consume significant power, leading to high operating costs and poor Power Usage Effectiveness (PUE).
Limited cooling capacity: Air cooling struggles to manage extreme heat loads in high-performance computing (HPC) and AI workloads.
To overcome these challenges, liquid cooling technologies have emerged, offering higher thermal efficiency and better scalability.
Direct-to-Chip
Liquid Cooling
Direct-to-chip cooling (also known as cold plate cooling) improves thermal management by circulating a liquid coolant directly over heat-generating components such as CPUs, GPUs, and memory modules.
How Direct-to-Chip Cooling Works
Cold plates are mounted directly onto processors or other high-power components.
A closed-loop liquid coolant system circulates a dielectric or water-based coolant through the cold plates, absorbing heat.
The heated coolant is then transferred to a heat exchanger, where it is cooled before being recirculated.
Advantages
- Higher efficiency than air cooling due to better thermal conductivity of liquids.
- Targeted cooling reduces hotspots and improves performance.
- Lower energy costs as liquid cooling reduces reliance on high-powered air conditioning.
Challenges
- Requires specialized infrastructure, including pumps, tubing and liquid distribution units.
- Risk of leaks if not properly designed and maintained.
- Only cools specific components, leaving other parts of the system reliant on air cooling.
While direct-to-chip cooling significantly improves efficiency, it is still a hybrid approach that combines liquid and air cooling. For maximum efficiency and simplicity, immersion cooling has emerged as a fully liquid-based solution.
Immersion Cooling
A Detailed Breakdown
Immersion cooling is a cutting-edge technology where entire server components are submerged in a thermally conductive, dielectric liquid that absorbs heat directly from the hardware. This method eliminates the need for traditional air cooling, fans, and heat sinks, offering significant energy savings and improved thermal management.
Types of
Immersion Cooling
There are two primary types of immersion cooling:
single-phase and two-phase cooling.
a. Single-Phase Immersion Cooling
In single-phase immersion cooling, IT hardware is submerged in a dielectric liquid that remains in its liquid state throughout the cooling process.
The heat generated by the hardware is absorbed by the coolant.
A pump or natural convection circulates the heated liquid to a heat exchanger, where it is cooled by an external cooling loop (e.g., chilled water or ambient cooling).
The cooled liquid is recirculated back into the immersion tank.
Advantages
- Simplicity and reliability: No phase change reduces maintenance complexity.
- Compatibility with existing infrastructure: Easier to implement in conventional data centers.
- Low maintenance costs: No need for refrigerants or high-pressure cooling loops.
b. Two-Phase Immersion Cooling
In two-phase immersion cooling, a specialized dielectric fluid is used, which boils at relatively low temperatures (~50-60°C). The process works as follows:
Heat from the IT hardware causes the fluid to vaporize.
The vapor rises and condenses on a heat exchanger positioned above the liquid surface.
The condensed liquid then drips back into the tank, completing the cycle.
Advantages
- Higher thermal efficiency: The phase change process removes heat more effectively.
- Greater heat dissipation capacity: Ideal for ultra-high-density computing environments.
- Reduced pumping requirements: Natural convection from the boiling process minimizes energy use.
Challenges
- Higher costs due to specialized dielectric fluids and complex system design.
- Fluid loss over time as small amounts of vapor escape.
- More challenging maintenance due to the boiling process and vapor management.
Key Benefits
of Immersion Cooling
Regardless of the specific approach, immersion cooling offers several distinct advantages over traditional cooling methods:
01. Drastic
Energy Savings
- Eliminates fans and reduces reliance on air conditioning.
- Lower PUE (often approaching 1.03–1.10 vs. 1.4–2.0 for traditional air cooling).
- Can leverage free cooling in many climates without expensive chillers.
02. Superior
Thermal Performance
- More uniform heat dissipation across IT hardware.
- Can handle higher power densities without risk of overheating.
- Reduces hotspots and thermal cycling, increasing component lifespan.
03. Increased
Hardware Reliability
- No moving parts inside the server (fans removed).
- Protects electronics from dust, humidity, and oxidation.
- Reduces mechanical stress on components.
04. Space Efficiency
- Compact immersion tanks allow for higher density deployments.
- Eliminates bulky air-cooling infrastructure (raised floors, CRACs, ductwork)
05. Eco-Friendliness
- Reduces water consumption compared to traditional cooling towers.
- Can use waste heat recovery for secondary applications (e.g., district heating).
Challenges & Adoption Considerations
Despite its advantages, immersion cooling is still an emerging technology with some adoption barriers:
Upfront costs are higher than conventional cooling due to the need for specialized tanks, fluids, and infrastructure.
Fluid compatibility concerns exist, as not all IT components are designed for immersion.
Vendor lock-in risk, as immersion fluids and solutions are not yet standardized across all hardware manufacturers.
However, as high-performance computing, AI workloads, and edge computing demand more efficient cooling solutions, immersion cooling is gaining traction in data centers worldwide.
Conclusion
Immersion cooling represents a transformative shift in data center thermal management, offering unmatched energy efficiency, thermal performance, and space optimization. While air cooling and direct-to-chip liquid cooling remain viable solutions for many environments, immersion cooling provides a future-proof approach for handling extreme computing loads. As data center energy costs and sustainability concerns continue to rise, immersion cooling is poised to become a mainstream solution in next-generation infrastructure.
The Evolution of TDP in CPUs and GPUs:
The Rising
Power Demands
of AI & HPC
The Power Consumption Challenge
Over the past decade, the power consumption of CPUs and GPUs has increased dramatically, driven by the growing demands of high-performance computing (HPC), artificial intelligence (AI), and data-intensive workloads. Traditional computing power consumption followed a steady growth curve, but the advent of AI accelerators, deep learning workloads, and high-density computing has caused a skyrocketing increase in total power per chip and per server.
This surge in power consumption has created significant challenges for thermal management, energy efficiency, and data center sustainability. In this section, we explore the historical evolution of CPU and GPU thermal design power (TDP), the impact of AI workloads on power density, and how these trends are pushing traditional cooling solutions to their limits.
Evolution of CPU & GPU TDP Over the Last Decade
CPU TDP Growth:
From 100W to 500W
Thermal Design Power (TDP) is a measure of the maximum heat a processor is expected to generate under normal workloads.
Over the past decade, CPUs have seen a significant increase in TDP, driven by rising core counts, higher clock speeds, and increased power draw for AI inference and high-performance computing.
(Server CPUs) TDP
(Server CPUs) TDP
(Intel Core & AMD Ryzen) TDP
Server CPUs have transitioned from ~140W to over 400W per socket.
Consumer CPUs, once limited to 95-125W, now exceed 250W under load.
The introduction of AI acceleration in CPUs (e.g., Intel AMX, AMD XDNA) has further increased power draw.
GPU TDP Growth:
The AI Power Explosion
GPUs have seen an even more dramatic increase in power consumption, particularly with the rise of AI accelerators and deep learning workloads.
While GPUs were originally designed for graphics processing, modern AI workloads require thousands of matrix multiplications per second, leading to higher power densities and thermal loads.
(Tesla /A100/H100) TDP
Datacenter GPUs TDP
(RTX/Radeon) TDP
Datacenter GPUs have increased from ~235W in 2014 to nearly 1000W in 2024.
The NVIDIA H100, widely used for AI training, can consume up to 700W per unit.
AMD's MI300X AI accelerator is expected to exceed 600W.
Consumer GPUs now approach 500-600W, up from 250W a decade ago.
AI Workloads: The Catalyst for Skyrocketing Power Demands
AI models like GPT-4, Llama, and Stable Diffusion require massive amounts of computation, pushing GPU clusters to the limit.
Training a single AI model can consume megawatts of power, equivalent to the electricity usage of entire small towns.
AI inference workloads, deployed at scale across cloud providers like AWS, Google, and Azure, further add to global data center energy consumption.
The Explosion of Server Power Consumption
The rise of AI, HPC, and hyperscale cloud computing has led to unprecedented increases in power consumption per server, per rack, and per data center.
Server Power Consumption Trends
(CPU & GPU Combined)
Datacenter GPUs TDP
AI Clusters and Mega-Racks
AI supercomputers, like Meta’s Research AI Cluster (RSC) and Microsoft’s OpenAI clusters, consume tens of megawatts.
High-density AI racks now exceed 100kW per rack, compared to 10-20kW per rack in traditional data centers.
Hyperscale AI data centers require massive cooling solutions, far beyond what traditional air cooling can provide.
The Limits of Traditional Cooling
With server power exceeding 10kW+ per node and AI racks surpassing 100kW, traditional air cooling and direct-to-chip solutions are reaching their practical limits. The key issues include:
Air cooling cannot efficiently dissipate 500W+ per chip without excessive fan speeds, noise, and inefficiency.
Water cooling (direct-to-chip) is effective, but still requires heat sinks and complex tubing.
Higher densities (20-30kW per server) mean traditional cooling becomes infeasible due to space, energy, and thermal constraints.
This is why immersion cooling is emerging as the dominant solution for future AI and HPC workloads. By fully submerging hardware in dielectric liquid, immersion cooling enables ultra-high-density AI compute with lower energy costs and minimal cooling infrastructure.
The Future:
Why Immersion Cooling
is Inevitable
With CPU and GPU power skyrocketing, immersion cooling is no longer a niche solution, it is becoming a necessity for high-performance AI workloads. The key drivers include:
Ultra-high
thermal efficiency
→ Can cool 100kW+ racks with minimal energy.
No moving parts
inside the server
→ Eliminates fan failures, vibration, and dust issues.
Supports
AI and HPC at scale
→ Designed for 500W-1000W GPUs and future megawatt-scale AI clusters.
Lower total cost
of ownership (TCO)
→ Reduces cooling infrastructure and energy costs.
As AI power demands continue to rise, immersion cooling is the only sustainable path forward for next-generation data centers.
