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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Bay Compute is developing software that coordinates data center operations to manage electricity use. Its co-founder, Vijay Gadepally, told EE Times that the system can reduce power use by up to 20%. That figure is a company-reported claim, not an independently verified result: the report names no customer, baseline, workload, or measurement method.
What Bay Compute’s AI is meant to do
Bay Compute describes its product as an “agentic operating system” for data centers. Rather than adding a new chip or cooling device, the software is intended to act as a supervisory layer: observe conditions across a facility and coordinate operating choices across IT hardware and supporting systems. In its own product description, the company lists hardware power, HVAC, energy storage, generators, and grid needs as areas the system can coordinate. That is vendor-authored positioning, not independent evidence of performance.
Gadepally compared the supervisory idea to a thermostat: the software takes in changing conditions and adjusts controls accordingly. The practical premise is that data center equipment and facility systems affect one another, while operators may otherwise manage them separately. Coordination could create opportunities to shift or limit consumption without treating every server or cooling system as an isolated control problem.
How power capping fits in
One example Gadepally gave EE Times is power capping: setting a limit on the power delivered to a processor. The cap can be adjusted as operating conditions change, potentially lowering the processor’s power draw. Gadepally said this can have relatively little effect on performance, but the report does not provide benchmark data to establish how much performance changes across workloads or how often that trade-off holds.
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Power capping is therefore best understood as a possible control technique, not a guarantee of energy savings with no slowdown. Its value depends on what workload is running, how much performance headroom it has, the cap selected, and how the system measures the result. The EE Times account does not specify those deployment details for Bay Compute’s reported savings.
Cooling options have different designs and trade-offs
Cooling is another part of data center operations that affects power demand. The EE Times report describes three approaches; it provides no comparative efficiency measurements, cost figures, or universal ranking.
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| Approach | How it removes heat | Scale described | Consideration raised in the report |
|---|---|---|---|
| Air cooling | Cold air is drawn across hot chips. | Air is directed at the equipment. | The report gives no comparative cost or efficiency data. |
| Liquid cooling | Cold water runs across chips. | Cooling is applied at the chip level. | The report gives no comparative cost or efficiency data. |
| Immersion cooling | Processors or an entire server are placed in a non-conductive liquid, such as mineral oil. | Can involve a server rather than just the chip. | Gadepally raised warranty concerns for expensive servers and described immersion as potentially more efficient; these are his views, not a universal finding. |
Gadepally suggested broader rack-level use of immersion cooling could take years. That is his forecast in the interview, not an established timeline for the industry. The available account does not establish which cooling approach is best for a given facility.
What the “up to 20%” figure does—and does not—show
EE Times attributes the “up to 20%” power-savings claim to Bay Compute co-founder Vijay Gadepally and says early systems were installed at unnamed global colocation data centers. The report does not identify a customer or disclose the baseline, workload, measurement protocol, duration, or independent validation. It also does not clarify whether the figure refers to power at a particular moment or energy consumed over time. It should not be treated as a typical, guaranteed, or independently demonstrated saving.
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For a buyer evaluating an operational-control platform, a credible comparison would need to state the facility and workload conditions, the measurement boundary, the period measured, and any performance or reliability effects alongside the energy result. Those details are not supplied for this claim.
Why data center power has become a larger concern
AI infrastructure is expanding against a backdrop of rising investment and electricity demand. EE Times reported that global data center investment reached half a trillion dollars in 2024, nearly double the 2022 level, citing an April report from the International Energy Agency. The same EE Times article said data centers represented 4.4% of U.S. electricity demand and could approach three times that share by 2028, citing a December 2024 Lawrence Berkeley National Laboratory report. These are figures as attributed by EE Times; the underlying IEA and LBNL reports are not linked here.
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- A web service can be enabled to view sensor data directly through a browser. Support for the REST API protocol can also be enabled to return JSON data from the sensors. However, these two features are disabled by default for network security reasons, and can be enabled by configuration tool.
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The article also raised concerns about grid stress, backup generation, local water availability, noise, and limited transparency about data center locations. These are potential community and infrastructure issues, not conditions that apply to every facility. EE Times additionally reported that the U.S. AI Action Plan called for exploring advanced grid management and demand management for large electricity users during critical periods.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What remains unknown about Bay Compute’s results
The public account supports a description of Bay Compute’s approach and its co-founder’s claimed savings, but not a general conclusion about real-world performance. It names no customer and supplies no independent study or detailed results. Without information about deployment conditions and measurement, operators cannot tell whether the reported maximum would transfer to their own workloads or what trade-offs it involved.
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