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.