You're only using 5% of the GPUs you paid for As of April 2026, GPU utilization in enterprise Kubernetes clusters ranges between 5% and 30%. Despite costing $2 to $15 per hour depending on the hardware, most GPUs remain idle for the majority of the time. According to a Cast AI report, companies are spending up to 20× more than what they actually need for GPU compute. In the race to adopt AI, many organizations secure GPU capacity “just in case.”But simply holding onto that ca

If you've been running an AI service for any length of time, you've probably hit this question at some point. "Is using an API actually the cheaper option? Or would it be better to just buy a GPU and run it ourselves?" As model performance converges, cost has become the decisive battleground. Teams at every scale are starting to run the numbers on which approach is actually cheaper for their usage volume — and the answer changes significantly depending on how much you're act

As geopolitical conflicts intensify, the limitations of centralized AI infrastructure are becoming clear. This article explores why distributed infrastructure is emerging as a more resilient and necessary approach.

Google’s TurboQuant reduces KV cache memory usage in LLM inference without sacrificing accuracy. Learn why 80GB GPUs were needed—and why mid-range GPUs may now be enough.