{"id":122022,"date":"2026-07-19T08:00:00","date_gmt":"2026-07-19T06:00:00","guid":{"rendered":"https:\/\/aixia.se\/aiqu-and-mlops-how-to-stop-wasting-expensive-gpus\/"},"modified":"2026-07-19T08:00:00","modified_gmt":"2026-07-19T06:00:00","slug":"aiqu-and-mlops-how-to-stop-wasting-expensive-gpus","status":"publish","type":"post","link":"https:\/\/aixia.se\/en\/aiqu-and-mlops-how-to-stop-wasting-expensive-gpus\/","title":{"rendered":"AiQu and MLOps: How to Stop Wasting Expensive GPUs"},"content":{"rendered":"
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AiQu and MLOps: How to Stop Wasting Expensive GPUs<\/h1>\n

Blog |<\/span> Aixia<\/p>\n<\/div>\n

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95% of GPU capacity is sitting idle. Meanwhile, data scientists are lining up to run their models. AiQu is Aixia\u2019s solution to one of the most costly problems in the world of AI. <\/strong><\/p>\n<\/div>\n

It\u2019s 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.<\/strong> <\/p>\n

A recent report from Cast AI confirms what we at Aixia have observed for years\u2014on average, only 5%<\/strong> of the GPU capacity that companies pay for is actually used. The rest? Allocated but unused. Reserved but idle. Expensive but empty. <\/p>\n

Why is so much wasted?<\/h3>\n

The problem isn’t that companies are buying too many GPUs\u2014it’s that they’re treating them like traditional servers in a world where AI workloads operate completely differently.<\/p>\n

Here are the three most common culprits:<\/p>\n

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1. Static resource allocation<\/strong>
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. <\/p>\n

2. Manual booking via email and spreadsheets<\/strong>
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. <\/p>\n

3. No insight into utilization rates<\/strong>
Without a centralized overview, no one knows how much is actually being used. The budget is approved based on guesswork, not data. <\/p>\n<\/div>\n

What is AiQu\u2014and why is it different?<\/h3>\n

AiQu is Aixia\u2019s 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: <\/p>\n

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\ud83c\udfaf Intelligent GPU scheduling<\/strong>
AiQu dynamically distributes workloads across available GPUs based on priority, queue time, and resource requirements. Training, inference, and experimentation efficiently share the same pool. <\/p>\n

\ud83d\udcca Full observability<\/strong>
See exactly which models are running, who is using which resources, and how utilization rates change over time. No more guesswork\u2014just facts. <\/p>\n

\ud83d\udd12 Secure multi-tenancy<\/strong>
Different teams, projects, and customers can share the same infrastructure without accessing each other\u2019s data or models. This is critical for companies with high security requirements. <\/p>\n<\/div>\n

On-prem is the new cloud<\/h3>\n

One trend that is gaining momentum is the move back from the cloud. 67% of companies\u2019 AI workloads are now running outside of public clouds<\/strong> \u2014and there are good reasons for this:<\/p>\n