Australian energy technology company FlexSysAI today launched a pilot with ResetData, Australia’s national science agency CSIRO and The University of Queensland to use its platform to make data centre demand more flexible by shifting GPU-based AI workloads across locations and time without impacting service, easing pressure on the grid at times of high demand.
ResetData is providing the sovereign AI infrastructure and live AI Factory environment to test the technology in practice, with CSIRO and The University of Queensland supporting validation of the pilot.
The FlexSysAI platform has the potential to be a breakthrough for the rollout of data centres by optimising grid capacity and allowing more data centres to connect to current infrastructure. FlexSysAI’s early modelling estimates its platform has the potential to optimise and dynamically adjust workloads on NVIDIA H200 GPUs at ResetData by 20-50% within seconds of a grid signal
The FlexSysAI platform is now live, managing the power and workloads of a H200 cluster at ResetData’s AI-F1, Australia's first publicly available sovereign AI factory. CSIRO and The University of Queensland will independently analyse and evaluate data from the pilot to better understand the role flexible AI workloads could play in supporting electricity system reliability.
Electricity networks could serve additional data centre connections if those loads can be curtailed during peak stress periods (typically just 0.25%-5% of the year). However, network operators lack operational data on whether AI datacentres can reliably provide this flexibility.
The pilot uses FlexSysAI to categorise AI workloads into distinct "Flex Tiers," ensuring that critical tasks are protected while elastic training jobs provide reliable load-shifting.
At scale, FlexSysAI can address a major bottleneck to the rollout of digital infrastructure, connecting live electricity market and grid conditions with AI workload balancing. It may help data centre operators connect to the grid sooner, pay less for power, access renewable electricity when it is abundant, and get paid to support the grid, while allowing operators to maintain control over when and where workloads are shifted.
The pilot comes as governments around the world consider how to regulate data centre energy use, and manage growing AI infrastructure demand pipelines.
Victor Feoktistov, Co-founder of FlexSyAI said:
“Early testing has been extremely promising, showing our platform can rapidly respond to electricity market signals and shift AI workloads to when and where power is more readily available. This reduces strain on the energy system, lowers operator’s energy costs and unlocks additional capacity in existing infrastructure, meaning quicker connections to the grid.”
Marcel Zalloua, co-CEO of ResetData said:
“It’s great to be working with FlexSysAI, CSIRO and The University of Queensland on this pilot. As Australia’s sovereign AI infrastructure provider, we want to enable Australian innovation by providing the infrastructure to test and prove new ideas that can have a meaningful impact across the industry.
Dr Sean Lawrence, CSIRO Energy Systems Research Scientist, said:
"Data centres are forecast to become some of the largest single points of electricity demand, and unlocking demand flexibility could help support the grid during periods of stress. This pilot will provide valuable real-world evidence about whether flexible AI workloads can help support electricity system reliability as Australia's future energy system evolves."
Professor Frederik Geth, UQ-Springfield Chair in Energy, said:
“Our role at UQ is to take the operational data this pilot generates and use it to examine how data centre flexibility could be represented in future network planning and regulatory frameworks. That's a gap that's been missing from the conversation around AI data centre growth, and it's the piece of the puzzle that determines whether this kind of flexibility actually gets recognised and rewarded in how networks are planned.”
Read the original media release published by FlexSysAI.
