Databricks is announcing 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 lives.
According to Databricks, 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.
With AIR, customers can:
- Train at scale with hassle-free GPU infrastructure: Serverless, on-demand access to advanced GPUs backed by reliable multinode training, optimized performance, and battle-tested by 100,000-plus 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.
- 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.
Managing GPU infrastructure requires significant operational overhead, from capacity planning and node maintenance to networking and monitoring, the company said.
With AIR, Databricks takes on the operational work of provisioning GPUs, maintaining healthy infrastructure, and optimizing the workload, so teams can focus on training.
Get the GPU capacity you need, on demand. Databricks handles GPU procurement, cluster configuration, and networking so teams can start training without building and operating a full GPU platform.
Scale with confidence on healthy infrastructure. Databricks runs dozens of checks for GPU and network health before and during training, so workloads can scale to hundreds of GPUs reliably, the company said.
AI Runtime validates GPUs before scheduling, continuously monitors active workloads, and periodically tests idle GPUs to detect and remove unhealthy nodes.
AIR extends trusted data controls and governance to every GPU workload. Unity Catalog enforces consistent read/write permissions on all aspects of a GPU workload (data access, checkpoints, logs, etc.), so your GPU platform and data governance layer now speak the exact same language, said Databricks.
For more information about this news, visit www.databricks.com.