AI Runtime

Important

This feature is in Public Preview.

AI Runtime is a serverless GPU compute offering at Databricks intended for deep learning workloads. You can use AI Runtime to train and fine-tune custom models using your favorite frameworks and get state-of-the-art efficiency, performance, and quality.

Get started

Run your first workload in minutes using our quickstart guides.

Get started	Description
🚀 CLI quickstart	Coming from a Slurm or Kubernetes cluster? Train on AI Runtime from your terminal in minutes.
📓 Notebook quickstart	Attach your notebook to a GPU in seconds and develop interactively..
⚡ Ray	Coming from a Ray cluster? Run Ray on AI Runtime with dashboard support.
🧭 Overview	Learn the key things about AI Runtime in two minutes: available GPUs, how it works, and limitations.

Features

AI Runtime is a full-stack GPU platform, with many ways to connect to GPUs, built-in observability and debugging tools, and pre-built environments.

Connect to Serverless GPUs: Reach serverless GPUs from wherever you work.

Feature	Description
Notebooks	Attach a notebook to serverless GPU compute and develop interactively, with no cluster setup.
IDE over SSH	Connect from your IDE or terminal over an SSH tunnel to work on a GPU node directly.
AI Runtime CLI	Submit and manage distributed GPU training jobs from your laptop using YAML job configs.
Ray	Run Ray Core, Ray Data, Ray Train, and Ray Tune on serverless GPU compute.

Managing Dependencies: Control the Python and system libraries your workload runs with.

Feature	Description
Pre-built environments	Start from a minimal Standard environment or a Databricks AI environment pre-loaded with ML frameworks.
Docker support	Bring your own container image to run workloads with a custom dependency stack.

Loading data into GPUs: Feed training data to your GPUs efficiently.

Feature	Description
Efficient data loaders	Stream data from Unity Catalog volumes with fault-tolerant loaders built for high GPU utilization.

Debugging and Observability: Track experiments, inspect output, and diagnose failures.

Feature	Description
MLflow for deep learning	Track experiments, metrics, and runs with MLflow, and save model checkpoints to Unity Catalog volumes.
Logs viewer	View training output and monitor GPU resource usage while your code runs.
Debugging with agents	Use Genie Code to generate training code, resolve environment issues, and debug GPU failures.

Scheduling and real-time serving: Move from interactive development to scheduled jobs and endpoints.

Feature	Description
Productionize your jobs	Deploy training code with Declarative Automation Bundles, schedule runs, and build multi-task GPU and CPU workflows.
Serve your models	Deploy your trained models behind a scalable endpoint with Model Serving.

Examples

Clone end-to-end examples and run them on AI Runtime in minutes, from the CLI or in notebooks.

Example	Description
Ray Examples	Take your existing Ray project and run it on AI Runtime in minutes.
Training a tabular recommendation model	Train deep-learning recommender models, such as two-tower architectures.
Computer vision	Object detection and image classification workloads on GPUs.
Classic machine learning	GPU-accelerated XGBoost, time series forecasting, and other classic ML tasks.
Fine-tuning an OSS LLM	Fine-tune open-source large language models with LoRA, QLoRA, or full fine-tuning.

Learn more

Find helpful content: the latest product updates and how AI Runtime works under the hood.

Resource	Description
Product updates	See the latest AI Runtime product updates and announcements.
How AI Runtime works under the hood	Read how Databricks keeps GPUs reliable across AI Runtime.