Today, we’re excited to announce the General Availability of AI Runtime (AIR),bringing fully managed GPU infrastructure directly into Databricks so customers can train their most demanding models where their enterprise data already lives.

Too often, AI teams spend more time securing GPU capacity, managing failures, and waiting for experiments to launch than improving their models. Running GPU infrastructure separately from their Lakehouse adds governance gaps and puts sensitive training data at risk. Ascoding agents drive more training experiments, these bottlenecks andrisks grow — making reliable, fast, and governed infrastructure essential.

AIR brings GPU infrastructure, developer tools, and data governance together so teams can train at scale, iterate faster, and secure every workload without managing a separate GPU stack. Hundreds of companies, including Robinhood, PicPay, Merck,Flo Health, TinyFish, andFactSet, already use AIR to train and ship deep learning models to production, with use cases spanning healthcare, manufacturing, and consumer-facing agents. Our ownDatabricks AI Research team uses AIR to train the models behind KARL and AI Parse.

With AIR, customers can:

• •**Train at scale with hassle-free GPU infrastructure:**Serverless, on-demand access to advanced GPUs backed byreliable multinode training, optimizedperformance, and battle-tested by 100,000+ GPU-hour training runs.
• •**Experiment faster with near-instant launch latency and easy debugging:**Bring your own code, containers, and Ray workloads – and obtain seconds-level launch latency on RDMA-enabled GPUs. Environment caching and built-in debugging tools to save time on every training loop.
• •**Secure and govern every GPU workload:**AIR operates within the governance perimeter of Databricks and Unity Catalog, and is backed by years of investment in Serverless security. AIR helps ensure GPU workloads protect valuable training data, comply with access policies, and are tracked/auditable.