---
title: AI Runtime example notebooks | Databricks on AWS
source: https://docs.databricks.com/aws/en/machine-learning/ai-runtime/examples/
ingestedAt: "2026-06-18T08:08:27.466Z"
---

Public Preview

AI Runtime for single-node tasks is in [Public Preview](https://docs.databricks.com/aws/en/release-notes/release-types). The distributed training API for multi-GPU workloads remain in [Beta](https://docs.databricks.com/aws/en/release-notes/release-types).

The pages below include various notebook examples that show how to use AI Runtime for different tasks.

*   *   [Large language models (LLMs)](https://docs.databricks.com/aws/en/machine-learning/ai-runtime/examples/gpu-llms)
    *   Examples for fine-tuning large language models including parameter-efficient methods.
*   *   [Computer vision](https://docs.databricks.com/aws/en/machine-learning/ai-runtime/examples/gpu-computer-vision)
    *   Examples for computer vision tasks including object detection and image classification.
*   *   [Deep learning based recommender systems](https://docs.databricks.com/aws/en/machine-learning/ai-runtime/examples/gpu-recommendation)
    *   Examples for building recommendation systems using modern deep learning approaches like two-tower models.
*   *   [Classic ML](https://docs.databricks.com/aws/en/machine-learning/ai-runtime/examples/gpu-classic-ml)
    *   Examples for traditional machine learning tasks including XGBoost model training and time series forecasting.
*   *   [Multi-GPU distributed training](https://docs.databricks.com/aws/en/machine-learning/ai-runtime/examples/gpu-distributed-training)
    *   Examples for scaling training across multiple GPUs and nodes using the Serverless GPU API.
