AiQu and MLOps: How to Stop Wasting Expensive GPUs
Blog | Aixia
95% of GPU capacity is sitting idle. Meanwhile, data scientists are lining up to run their models. AiQu is Aixia’s solution to one of the most costly problems in the world of AI.
It’s easy to be fascinated by the latest large language model or impressive image generator. But behind the scenes of nearly every AI project lies a far less glamorous problem: the GPUs are sitting idle even though the teams are crying out for computing power.
A recent report from Cast AI confirms what we at Aixia have observed for years—on average, only 5% of the GPU capacity that companies pay for is actually used. The rest? Allocated but unused. Reserved but idle. Expensive but empty.
Why is so much wasted?
The problem isn’t that companies are buying too many GPUs—it’s that they’re treating them like traditional servers in a world where AI workloads operate completely differently.
Here are the three most common culprits:
1. Static resource allocation
At midnight, no training runs are taking place, but the GPUs are still locked to teams that are inactive. At lunchtime, all resources are in use and the queue is growing.
2. Manual booking via email and spreadsheets
Data scientists request GPU time in Slack threads. Someone forgets to release their booking. A week later, they discover that an entire DGX has been sitting unused.
3. No insight into utilization rates
Without a centralized overview, no one knows how much is actually being used. The budget is approved based on guesswork, not data.
What is AiQu—and why is it different?
AiQu is Aixia’s MLOps platform, built for organizations that refuse to let their most expensive investments sit idle. The platform brings together three critical functions under one roof:
🎯 Intelligent GPU scheduling
AiQu dynamically distributes workloads across available GPUs based on priority, queue time, and resource requirements. Training, inference, and experimentation efficiently share the same pool.
📊 Full observability
See exactly which models are running, who is using which resources, and how utilization rates change over time. No more guesswork—just facts.
🔒 Secure multi-tenancy
Different teams, projects, and customers can share the same infrastructure without accessing each other’s data or models. This is critical for companies with high security requirements.
On-prem is the new cloud
One trend that is gaining momentum is the move back from the cloud. 67% of companies’ AI workloads are now running outside of public clouds —and there are good reasons for this:
- Data Residency: Sensitive data never leaves the building
- Predictable costs: No surprise bill of $50,000 for “egress charges”
- Full control: You own the hardware, the infrastructure, and policy-making
- Sustainability: Swedish data centers powered by fossil-free electricity outperform cloud regions that rely on coal-based energy
AiQu is designed for this reality—a platform that optimizes your on-premises GPU infrastructure just as seamlessly as any hyperscale service.
The market is growing—are you growing too?
The MLOps market is projected to grow from $3.3 billion in 2026 to over $89 billion in 2034. This is no coincidence. Companies are realizing that the models are inexpensive—it’s the infrastructure that’s expensive.
Whoever manages to run more experiments, train more models, and deploy faster with the same hardware budget will win the AI race. Whoever continues to reserve GPUs via email will lose it.
“The problem isn’t that we don’t have GPUs. The problem is that we don’t know which ones are available, who has reserved what, and whether anyone is even using them.” – That’s how an AI manager at a major Swedish industrial company describes his day-to-day work before AiQu.
🚀 Start optimizing your GPUs today
At Aixia, we help you get started with AiQu—whether you have a handful of GPUs or an entire DGX SuperPOD. We assess your current usage, identify inefficiencies, and build an optimized MLOps platform tailored to your needs.
Published by Aixia | 2026
