---
type: concept
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  pageDirectory: concepts
  sources:
    - computer-vision-databricks-on-aws.md
  confidence: 0.9
  provenanceState: extracted
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  aliases:
    - serverless-gpu-for-machine-learning
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  citations:
    - file: computer-vision-databricks-on-aws.md
title: Serverless GPU for Machine Learning
description: On-demand GPU compute resources on Databricks that enable training and fine-tuning of computer vision models without managing underlying infrastructure.
tags:
  - compute
  - gpu
  - serverless
timestamp: "2026-06-19T09:20:21.129Z"
---

# Serverless GPU for Machine Learning

**Serverless GPU for Machine Learning** refers to on-demand, auto-scaling GPU compute resources that are managed entirely by the cloud platform, eliminating the need for users to provision, configure, or manage GPU clusters. This paradigm enables data scientists and ML engineers to run training and inference workloads without infrastructure overhead.

## Overview

Serverless GPU computing abstracts away the underlying hardware management, allowing users to focus on model development rather than cluster administration. In the context of machine learning, serverless GPUs are particularly valuable for tasks that require intermittent or burstable GPU access, such as experimentation, fine-tuning, and small-to-medium scale training jobs. ^[computer-vision-databricks-on-aws.md]

## Key Capabilities

### Single-Node Training

Serverless GPU environments support single-node training tasks, which are ideal for model experimentation, prototyping, and smaller-scale workloads. These environments automatically handle resource allocation and deallocation based on workload demands. ^[computer-vision-databricks-on-aws.md]

### Computer Vision Workloads

Serverless GPUs are well-suited for computer vision tasks, including:

- **Image classification** using convolutional neural networks (CNNs)
- **Object detection** using models like RetinaNet and YOLO11n
- **Fine-tuning** pre-trained vision models on custom datasets

These workloads benefit from the parallel processing capabilities of GPUs for matrix operations common in deep learning. ^[computer-vision-databricks-on-aws.md]

## Example Workflows

### Image Classification

Training a 2D convolutional neural network for image classification on serverless GPUs follows a straightforward workflow: load the dataset, define the model architecture, configure training parameters, and execute the training loop. The serverless infrastructure handles GPU allocation automatically. ^[computer-vision-databricks-on-aws.md]

### Object Detection

Object detection models like RetinaNet and YOLO11n can be trained on serverless GPUs using standard datasets such as COCO128. These training pipelines typically include:

- Dataset loading and preprocessing
- Model configuration and initialization
- Training with GPU acceleration
- Model tracking with [MLflow](/concepts/mlflow.md)
- Deployment to [Model Serving](/concepts/model-serving.md) for inference

^[computer-vision-databricks-on-aws.md]

## Integration with MLflow

Serverless GPU training workflows integrate with [MLflow](/concepts/mlflow.md) for experiment tracking, model versioning, and deployment. After training completes, models can be registered in the MLflow Model Registry and deployed to production serving endpoints. ^[computer-vision-databricks-on-aws.md]

## Benefits

- **No infrastructure management**: Users do not need to provision or configure GPU clusters
- **Automatic scaling**: Resources scale up and down based on workload demands
- **Cost efficiency**: Pay only for compute time used, with no idle cluster costs
- **Rapid experimentation**: Quickly spin up GPU environments for testing and iteration

## Limitations

- **Multi-GPU workloads**: Distributed training across multiple GPUs may require different infrastructure configurations
- **Long-running jobs**: Extended training runs may be better suited for dedicated GPU clusters
- **Resource availability**: Serverless GPU capacity may be limited during peak demand periods

## Related Concepts

- GPU Scheduling – Optimizing GPU utilization for training and inference
- [Databricks Runtime for Machine Learning](/concepts/databricks-runtime-for-machine-learning.md) – Pre-built runtime with GPU support
- [A100 GPU Support on Databricks](/concepts/a100-gpu-support-on-databricks.md) – High-performance GPU instances for deep learning
- [Model Serving](/concepts/model-serving.md) – Deploying trained models for inference
- [Distributed Training](/concepts/workload-yaml-for-distributed-training.md) – Multi-GPU and multi-node training approaches

## Sources

- computer-vision-databricks-on-aws.md

# Citations

1. [computer-vision-databricks-on-aws.md](/references/computer-vision-databricks-on-aws-14d18d47.md)
