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.