Technology
Celero Infrastructure powers AI and data-centre growth, where demand for reliable, always-available electricity is growing faster than the grid can accommodate.
Innovation that expands what’s possible
Celero Infrastructure’s mission is to build the enabling infrastructure for both Australia’s energy transition and its digital acceleration. Those two imperatives are directly connected: AI and data-centre growth require large amounts of reliable, always-available electricity, and that demand is growing faster than the grid can accommodate.
Our partnership with Mindbeam is a direct expression of that mission. Mindbeam is a US-headquartered AI infrastructure startup focused on making large language model training and inference dramatically faster and cheaper.
Mindbeam’s Litespark framework enables AI operators to significantly reduce energy consumption in large scale model training and inference without changing hardware or rewriting code. Faster training cycles mean your teams iterate quicker and better GPU utilisation means significant cuts to your compute costs. A reduction in GPU energy use allows operators to meaningfully expand compute output within existing approved power envelopes. This can defer costly infrastructure upgrades and reduce exposure to grid connection limitations.
What Mindbeam Litespark does
Litespark is a software-layer framework that uses optimisation algorithms to improve how Graphics Processing Unit (GPU) clusters execute AI training and inference workloads. It integrates with existing environments alongside PyTorch, TensorFlow, and JAX, delivering performance improvements that compound as workloads increase.
It operates at the orchestration layer of AI infrastructure, coordinating distributed compute across GPU clusters to reduce idle processing time and improve utilisation efficiency across nodes. For Data Centre operators and Neocloud providers, your sensitive and government tenants can use Litespark inference to run production AI using sovereign, on-premise hardware.
Benchmarks show up to a 6x improvement in training speed on a 30B model across 128 NVIDIA H200 GPUs, with energy consumption reduced by up to 86% in enterprise multi-node deployments. These results have been independently validated by Monash University’s Faculty of Information Technology and tested in a live Amazon Web Services (AWS) environment, not a controlled lab. Zero code changes are required: no re-architecting models, no rewriting pipelines, no new hardware.
“By partnering with Celero and subjecting our technology to Monash’s independent validation, we’ve demonstrated that software-layer optimisation is the most immediate and effective tool for preserving grid stability while accelerating the AI revolution.”
– Nii Osae, Mindbeam CEO & Founder
No hardware or code changes required
Fast, docker image deployment
For data centre operators
and GPUaaS providers
- Offer measurably greener, faster LLM training as a commercial differentiator when competing for enterprise and heavy-compute tenants
- Litespark is a tangible, validated capability, not a sustainability aspiration.
For enterprise AI
and ML teams
- Reduce one of the largest cost drivers in large-scale AI training: energy and compute inefficiency
- Achieve efficiency gains that compound across every training run
- Improve time-to-training and model iteration speed without workflow disruption.
For cloud and managed
services providers
- Offer Litespark as a managed optimisation layer for AI customers
- Deploy as a software-only enhancement with no disruption to customer workflows
- Scale efficiently across existing GPU infrastructure without hardware changes.
Policy and
ESG alignment
- Supports government and industry expectations for improved data centre efficiency and responsible energy use
- Helps organisations demonstrate measurable reductions in compute energy intensity
- Positions customers ahead of emerging regulatory and sustainability requirements
No hardware or code changes required
Fast, docker image deployment
For data centre operators
and GPUaaS providers
- Offer measurably greener, faster LLM training as a commercial differentiator when competing for enterprise and heavy-compute tenants
- Litespark is a tangible, validated capability, not a sustainability aspiration.
For enterprise AI and ML teams
- Reduce one of the largest cost drivers in large-scale AI training: energy and compute inefficiency
- Achieve efficiency gains that compound across every training run
- Improve time-to-training and model iteration speed without workflow disruption.
For cloud and managed
services providers
- Offer Litespark as a managed optimisation layer for AI customers
- Deploy as a software-only enhancement with no disruption to customer workflows
- Scale efficiently across existing GPU infrastructure without hardware changes.
Policy and
ESG alignment
- Supports government and industry expectations for improved data centre efficiency and responsible energy use
- Helps organisations demonstrate measurable reductions in compute energy intensity
- Positions customers ahead of emerging regulatory and sustainability requirements