Databricks AI Runtime launches with expanded NVIDIA collaboration
Databricks announced at GTC Berlin that AI Runtime is generally available, expanding its collaboration with NVIDIA for customizing open models with enterprise data. Developers can use NVIDIA NeMo, NGC containers, and Nemotron cookbooks to train and fine-tune models on serverless NVIDIA GPUs, then evaluate them and deploy them through Databricks Model Serving. Databricks also says AI Runtime supports distributed training, governed data access through Unity Catalog, experiment tracking with MLflow, and custom containers.
The release gives enterprises a managed path from governed data to customized open-model training, fine-tuning, evaluation, and real-time deployment, though performance and adoption benefits remain vendor-reported.
GPUs power today's most advanced AI workloads, from cutting-edge language models to multimodal AI systems and agents. Increasingly, enterprises are adapting open models to their own data and business needs. But securing GPU access is only part of the challenge. Teams also need to configure training jobs, govern data access, and manage GPU infrastructure, leaving less time to experiment with models and evaluate how well they meet business needs.
Databricks and NVIDIA provide a seamless path from enterprise data to customized AI models, from fine-tuning smaller models to large-scale distributed training. Through Unity Gateway, Databricks provides developers with governed access to frontier and open-source models. For teams that need to customize models, AI Runtime (AIR) brings NVIDIA’s AI software ecosystem and serverless GPUs to enterprise data. Teams can experiment, fine-tune, and evaluate models on AIR, then deploy them for real-time inference through Databricks Model Serving — all within the Databricks Platform, with less infrastructure and governance work to manage.
Today at GTC Berlin, Databricks and NVIDIA are expanding their collaboration to make it easier for enterprises to customize open models. Databricks AIR is now generally available: developers can use NVIDIA NeMo, NGC containers, and new Nemotron cookbooks to train and fine-tune open models on serverless NVIDIA GPUs. Together, these capabilities give teams more flexibility to adapt models to business-specific tasks and put them to work in applications and agents.
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