Ask an AI engineering lead what gives them the most trouble these days and the model itself rarely comes up. The real problem sits one layer below, in the infrastructure, and it comes down to a single question: how much GPU capacity should we actually secure? A March 2026 Akamai survey of 200 AI practitioners put a number on that feeling. 65.9% of AI-native teams named GPU capacity planning the hardest challenge they face when scaling, ranking it above every other operational

There is a GPU in the development workstation, and another one in the lab server. The edge box at a branch office may have an accelerator in it too. But the moment you try to load a model, the story changes. The driver version may not match, the SDK you need may be missing, or a network restriction may block access from outside. The hardware is right there, and yet the service cannot use it. Kitae Yoo of the AIEEV Dev Team builds the distributed infrastructure that connects

Part 1 looked at why AI agents are a 'program-shaped workload' that breaks the assumptions of existing serving systems, and at how the world's top systems conferences are approaching the problem. As the CacheBlend case showed, much of the answer depends on resources outside GPU memory, namely the DRAM and SSD that consumer PCs already have. Let's return to the cost question and continue from there, looking at why that direction converges on the picture of a "consumer GPU clou

Hello. I'm Yongrae Cho, and I lead AI infrastructure research and development on AIEEV's engineering team. I hold a PhD in Computer Science focused on the scalability and reliability of distributed systems, and I now work on the efficiency and reliability of LLM serving on Air Cloud, along with research and development in AI agent technology. As more services, like AI agents, call a model dozens or even hundreds of times for a single request, the inference bill grows far fast