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

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

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

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

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.