Cooling a GPU data center as one big room wastes energy, because heat is never spread evenly. ProphetStor’s patented method predicts where and when workloads will generate heat, then decides for each section of the data center whether to move the workload to where cooling is already sufficient, or to tune the cooling where the workload is, whichever uses less energy. Fan speeds, liquid coolant flow, and air-conditioning settings are adjusted ahead of demand, so every rack gets the cooling it needs, exactly when it needs it, without over-provisioning.
The Cooling Problem in GPU Data Centers
Traditional cooling reacts to heat. Predictive Workload-Aware Cooling plans for it.
30 - 40%
Share of a GPU data center’s electricity spent on cooling and air conditioning
Up to 30%
Energy savings with ProphetStor’s predictive, workload-aware cooling
In modern GPU data centers, cooling is the second-largest energy bill after the servers themselves. Yet most cooling systems still run on fixed setpoints and simple temperature feedback.
Reactive by Design
Conventional cooling waits for a temperature sensor to cross a threshold, then responds. GPU workloads ramp up in seconds, while pumps, fans, and chillers take minutes to catch up. The result is hotspots during the ramp and wasted energy once it passes.
One Room, One Setting
Most cooling strategies manage the temperature and humidity of the whole facility. But heat is uneven: one row runs a training job at full power while the next sits half idle. Cooling everything to the level of the hottest rack wastes energy everywhere else.
Blind to the Workload
Cooling controls and workload schedulers live in separate worlds. The scheduler places jobs without knowing which sections have spare cooling capacity, and the cooling system adjusts without knowing which jobs are about to start. Neither can make the energy-optimal choice alone.
What It Is
Predictive Workload-Aware Cooling is ProphetStor’s patented method for forecasting the workload, and therefore the heat, in each section of a data center, and using that forecast to cool each section precisely, before the heat arrives. (U.S. Patent)
Think of a building where one meeting room is getting hot. Instead of turning up the air conditioning for the entire building, you have two smarter options: move the meeting to a room that is already cool, or cool only that one room. This patent makes that choice automatically, for every rack section, every planning cycle, based on which option uses less energy.
Option A: Move the Workload
If another section already has spare cooling capacity, the predicted workload is placed there. The heat lands where the cooling already exists, and no extra cooling energy is spent. Unused or underutilized servers can be put into hibernation, and cooling in those sections is turned down.
Option B: Tune the Cooling
If moving the workload is not the better choice, the cooling resources in that section are adjusted ahead of time: fan speeds, liquid coolant flow rates, and air-conditioning setpoints. Sections running below capacity receive less cooling, and sections about to heat up receive more, before temperatures rise.
The Decision Flow of Patented Predictive Workload-Aware Cooling | ProphetStor
How It Works
Four automated steps, running continuously, no manual intervention required.
STEP 01 · SENSE
Continuous Section-Level Monitoring
Sensors distributed across every section of the data center stream real-time data: temperatures, humidity, workload demand, system performance, and environmental conditions. Each section, at least one rack of servers with its own cooling resources, becomes its own managed zone.
STEP 02 · PREDICT
Workload and Heat Forecasting
Using historical and real-time data, the system forecasts future workload demand and the matching cooling requirement for each section, over both time and space. Prediction is powered by ProphetStor's patented Multi-Layer Correlation, which traces how a change in workload ripples down to GPU temperature and then to cooling demand.
STEP 03 · DECIDE
Move the Work or Tune the Cooling
For each predicted workload, the system compares two options: place it in a section that already meets its cooling needs, or adjust the cooling where it is. It selects whichever option results in lower energy use. Idle or underutilized servers can be hibernated at this stage.
STEP 04 · ADJUST
Just-in-Time Cooling
Cooling resources in each section are adjusted ahead of demand: fan speeds, liquid coolant flow rates, and air-conditioning settings. Results feed back into the models, which keep learning and refining over time. The outcome is optimal cooling without over-provisioning.
Why It Matters
Proactive
Cooling ready before the heat arrives, not after
Precise
Each section cooled to its own need, not the hottest rack’s
Energy-Optimal
Moves the work or tunes the cooling, whichever saves more
Automated
Continuous adjustment with no manual setpoint tuning
Traditional cooling tells you a rack got hot. ProphetStor’s Predictive Workload-Aware Cooling knows which rack is about to get hot, and makes sure it never does, using the least energy possible.
This is where IT and facilities finally meet. The same workload forecasts that drive GPU placement and autoscaling now drive the cooling plant, turning cooling from a fixed overhead into a resource that is scheduled as carefully as the GPUs themselves.
Conventional Cooling Control v.s. ProphetStor
Conventional Cooling Control
✕
Reactive: adjusts after temperature sensors cross a threshold
✕
Facility-wide: one temperature and humidity target for the whole room
✕
Workload-blind: no link between job scheduling and cooling capacity
✕
Fixed setpoints and simple rules that over-cool “just in case”
✕
Idle servers keep drawing power and cooling
Patented Predictive Workload-Aware Cooling
✓
Predictive: forecasts workload and heat before demand arrives
✓
Section-level: each rack section cooled to its own requirement
✓
Idle servers hibernated, and their sections’ cooling turned down
✓
Idle servers hibernated, and their sections’ cooling turned down
✓
Machine-learning models that keep refining over time
Where It Applies
The patented method works with the full range of data center cooling, including air cooling, liquid cooling, evaporative cooling, hybrid cooling, and free cooling. It powers Federator.ai Smart Liquid Cooling, a capability of Federator.ai Cortex.
High-density AI infrastructure
Liquid-Cooled GPU Clusters
Racks of high-performance GPU servers can draw around 10 kW per server, and training jobs swing between full load and idle. Adjusting coolant flow ahead of each ramp keeps GPUs within thermal limits without running pumps at maximum around the clock.
Mixed and retrofitted facilities
Hybrid Air and Liquid Cooling
Many data centers run air-cooled and liquid-cooled rows side by side. Section-level control lets each zone use the cooling method and setting that fits its workload, instead of one facility-wide target.
Multi-tenant and variable workloads
Shared and Fluctuating Capacity
When workloads shift across tenants and time zones, some sections sit underused while others overheat. Placing new work where cooling already exists, and hibernating idle servers, turns stranded cooling capacity into usable capacity.
U.S. Patent: Predictive Workload-Aware Cooling
ProphetStor was granted a U.S. patent for the method and system for optimizing cooling resources in a data center based on workload prediction. The patent covers the full pipeline: real-time data collection from sensors across data center sections, forecasting of workload demand and cooling requirements over time and space, the energy-based decision between relocating workloads and adjusting cooling, dynamic section-level adjustment of fan speeds, liquid cooling rates, and air-conditioning settings, and hibernation of underutilized servers.
Frequently Asked Questions
What is Predictive Workload-Aware Cooling?
It is ProphetStor’s patented method for optimizing cooling resources in a data center based on workload prediction. Instead of cooling the whole facility to a fixed target, it forecasts the workload and heat in each section, then either moves the workload to where cooling is already sufficient or adjusts the cooling in that section, whichever uses less energy. Fan speeds, liquid coolant flow, and air-conditioning settings are tuned before demand arrives.
How is it different from conventional data center cooling control?
Conventional cooling is reactive and facility-wide. It responds after temperature sensors cross a threshold and applies one setting to the whole room. ProphetStor’s method is predictive and section-level. It forecasts heat before it appears, cools each section to its own requirement, and connects cooling decisions to workload placement, which conventional controls cannot do.
Why decide between moving the workload and adjusting the cooling?
Because sometimes the cheapest cooling is the cooling you already have. If one section has spare cooling capacity, placing a new job there costs no extra cooling energy. If not, the system tunes the cooling in the section where the job runs. Comparing both options for every predicted workload lets the data center choose the lower-energy path automatically.
How does the system predict future workload and cooling needs?
What cooling systems does it work with?
The method covers the full range of data center cooling resources, including air cooling, liquid cooling, evaporative cooling, hybrid cooling, and free cooling. Adjustable parameters include fan speeds, liquid cooling flow rates, and air-conditioning setpoints. It integrates with existing HVAC and liquid cooling control systems through their APIs.
How much energy can it save?
Cooling and air conditioning typically account for 30–40% of a modern GPU data center’s electricity. By cooling only where and when it is needed, and by avoiding over-provisioning, ProphetStor’s predictive workload-aware cooling can achieve up to 30% energy savings, along with lower operating costs and a smaller carbon footprint.
Which ProphetStor product uses this technology?
The technology powers Federator.ai Smart Liquid Cooling, a capability of Federator.ai Cortex, ProphetStor’s AIOps platform for GPU data centers. Within Federator.ai Cortex, it works alongside ProphetStor’s other patented technologies, including Multi-Layer Correlation, Spatial-Temporal GPU Optimization, and Predictive Self-Driving Autoscaling, so workload forecasts drive GPU placement, scaling, and cooling together.