Hi, I'm CY Lee (이창윤), DevOps/SRE engineer on the dev team. I stopped by once before to talk about the ClawHub plugin — and now I'm back, this time to share a problem we ran into with service alerting and how we worked through it. How many notifications does your team receive in a day? User feedback, payment events, signup events, 500 errors from SigNoz. Each of these was arriving in a separate Slack channel. Keeping channels separated by type made it easy to find a specific c

The Distributed GPU Cloud Story and Why Ray Is at the Center of It 💡 Core Message "A GPU that never serves a request has no business value. " Air Cloud connects everything from the runtime layer to the platform layer, so hardware actually reaches users as a real service. Introduction When people talk about AI infrastructure today, the conversation usually starts with GPU scarcity. How many H100s did you lock in? Is B200 supply going to loosen up? Does your data center have e

Every GPU experiment starts the same way: before you write a single line of code, you're already fighting your environment. Matching CUDA versions, installing drivers, resolving package conflicts — two hours gone before anything actually runs. Air Cloud solves this by letting you deploy a container with PyTorch and CUDA pre-configured, then connect via SSH immediately. No local setup required. Add Claude Code into the mix, and you can start writing, debugging, and running cod

만약 혜택을 못받고 가입하셨거나, 프로모션 신청에 문제가 있는 경우 여기로 폼을 작성해주세요. 대학 연구실 & 학생 대상 Research Program, 5월 31일까지 연장됩니다! 🎉 지난 4월, 한 달간 진행한 대학 연구실과 학생들을 위한 프로모션 이벤트에 서울대, 고려대, 포스텍 등 9개 대학교에서 90명 이상의 학생 및 연구자분들이 프로모션에 신청해주셨습니다. 추가로 16개 대학 연구실에서도 프로그램에 참여해주시면서 예상보다 훨씬 뜨거운 반응에 힘입어 더 많은 분들과 함께하고자 프로모션 기간을 연장하기로 했습니다! 참여 방법 ✅ 가입만 해도 크레딧 즉시 지급! 기존에는 신청폼 작성 후 크레딧을 직접 등록해야 하는 번거로움이 있었는데요. 이번 연장을 계기로 프로세스를 전면 개선했습니다. 학교·연구기관(ac.kr) 계정으로 가입 시 온보딩과 동시에 10,000 AU 크레딧이 자동 충전됩니다. 별도의 신청이나 등록 절차 없이, 바로 10,00

Hi, I'm Jinbeom Kim, a Software Developer on the AIEEV Dev Team. I studied computer science through both undergrad and graduate school, and I've been with AIEEV since the early days of the company — working on how we can operate more distributed GPU resources efficiently within Air Cloud 😊 In this post, I want to walk through two techniques we regularly evaluate when thinking about how to deploy AI models more efficiently. The first is Quantization — a method for reducing me

AirCloud's April release is built around one goal: making it faster to run AI workloads, more reliable to operate them, and more flexible to put your existing GPU resources to work. This update includes enhanced Air Container operations, the general availability of Air API, Resource Provider (RP) support, and the introduction of an intelligent scheduler. Developers can now handle container access, log monitoring, error response, and API integration more seamlessly. Enterprise

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

Real AI utilization starts with infrastructure — the kind that lets anyone use AI as much as they need, whenever they need it. As AI adoption accelerates, so does the infrastructure market behind it. Providers worldwide are competing across different architectures, and AIEEV is part of that race. Our approach is different: instead of building on centralized data centers, we launched as a distributed cloud that connects idle GPU resources across a decentralized network. Today,

Hi,I’m CY Lee from the DevOps/SRE team. With the launch of Air API, we’ve been building out our internal infrastructure monitoring system. Along the way, we developed an OpenClaw plugin—and in this post, I’d like to walk you through what we built and why it matters. 🙂 Before We Start If you’ve used OpenClaw for a while, you’ve probably experienced something like this at least once. The moment you try to connect an external model provider, you find yourself going through the

This illustration was created with AI to support the explanation. A few days ago, Meta announced it was extending its AI cloud contract with CoreWeave through 2032, committing an additional $21 billion. Combined with the existing $14.2 billion agreement, the total comes to over $35 billion — roughly $35B locked in for GPU compute, years in advance. CoreWeave, as of the announcement date, became the fastest cloud company in history to reach $5 billion in ARR. The dollar fig

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

Across industries, job functions, and academia, more teams are building their own AI agent assistants and putting them to work. But the longer you run them, the harder it is to ignore one unavoidable reality: cost . An API invoice larger than your monthly subscription fee, quietly accumulating call by call, has become a familiar sight. AI agents don't call a model once per task. They call it tens or even hundreds of times per job -- planning, invoking tools, verifying results

If you've ever tried serving an open-source AI model yourself, you know the pain. Setting up GPU infrastructure takes longer than choosing the model itself. Provisioning GPUs, configuring environments, scaling with traffic... the road to running a single model is way too long. Air API eliminates that entire process. It's a serverless API service for open-source AI models. No infrastructure to build. Just an API key to get started. Key Features 💡 OpenAI-Compatible Endpoint

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.

AIEEV has launched a newly redesigned website to strengthen brand trust and better communicate its distributed cloud vision. Discover what’s changed and why it matters.

AIEEV has closed its Pre-A funding round with participation from Bluepoint Partners, ROWE Partners, and AI Angel Club, bringing total funding to $1.5M.

AIEEV, a Distributed AI GPU Cloud, Obtains International Information Security Standard ‘ISO 27001’ Certification

AIEEV Selected for LG Uplus’s AI Open Innovation Program ‘SHIFT’