---
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  provenanceState: extracted
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  aliases:
    - single-node-gpu-clusters-for-deep-learning
    - SNGCFDL
    - Single Node GPU cluster
    - single-node-gpu-cluster-for-deep-learning-development
    - SNGCFDLD
  citations:
    - file: best-practices-for-deep-learning-on-databricks-databricks-on-aws.md
title: Single Node GPU Clusters for Deep Learning
description: Best practice recommendation to use Single Node (driver-only) GPU clusters for fast, cost-effective deep learning model development, as a single node with multiple GPUs avoids network communication overhead of distributed training.
tags:
  - clusters
  - gpu
  - deep-learning
timestamp: "2026-06-19T14:09:33.274Z"
---

---
title: Single Node GPU Clusters for Deep Learning
summary: A single-node GPU cluster (driver only) is recommended for fast, cost-effective deep learning model development, especially for iterative work and small- to medium-size datasets.
sources:
  - best-practices-for-deep-learning-on-databricks-databricks-on-aws.md
kind: concept
createdAt: "2026-06-19T10:44:59.232Z"
updatedAt: "2026-06-19T10:44:59.232Z"
tags:
  - databricks
  - gpu
  - configuration
  - distributed-computing
  - best-practices
aliases:
  - single-node-gpu-clusters-for-deep-learning
  - SNGCDL
confidence: 0.95
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---

# Single Node GPU Clusters for Deep Learning

**Single Node GPU Clusters for Deep Learning** refers to a cluster architecture where a single compute node (the driver) is equipped with one or more GPUs, and no additional worker nodes are used. This setup is the recommended starting point for deep learning model development on Databricks because it offers the best balance of speed and cost for most iterative training tasks. ^[best-practices-for-deep-learning-on-databricks-databricks-on-aws.md]

## Overview

A Single Node cluster is a driver-only GPU cluster. In this configuration, all GPUs reside on a single machine, avoiding the network communication overhead that multi-node distributed training incurs. As a result, a single node with **4 GPUs** is typically faster for training than a four-worker cluster where each worker has one GPU, because the latter requires frequent data transfers across the network. ^[best-practices-for-deep-learning-on-databricks-databricks-on-aws.md]

## When to Use a Single Node GPU Cluster

Single Node GPU clusters are best suited for:

- **Fast, iterative development** – Data scientists can quickly prototype and test model architectures without the complexity of distributed training. ^[best-practices-for-deep-learning-on-databricks-databricks-on-aws.md]
- **Small- to medium-size datasets** – For datasets that fit comfortably on a single machine’s GPUs, this approach is the most cost-effective. ^[best-practices-for-deep-learning-on-databricks-databricks-on-aws.md]
- **Hyperparameter tuning and experimentation** – The simplicity of a single node makes it easier to run many small experiments in parallel or sequentially.

## Scaling Beyond a Single Node

When the dataset is large enough that training on a single machine becomes too slow, consider moving to **multi-GPU** and eventually **distributed compute** (multi-node) setups. Databricks provides tools such as [TorchDistributor](/concepts/torchdistributor.md), [DeepSpeed](/concepts/deepspeed.md), and Ray to facilitate this transition. ^[best-practices-for-deep-learning-on-databricks-databricks-on-aws.md]

## Best Practices

- **Start with a Single Node cluster** during development. This avoids the overhead of managing distributed training until it is actually needed. ^[best-practices-for-deep-learning-on-databricks-databricks-on-aws.md]
- **Use cluster policies** to guide data scientists toward appropriate choices, such as using a Single Node cluster for development and an autoscaling cluster for large jobs. ^[best-practices-for-deep-learning-on-databricks-databricks-on-aws.md]
- **Choose efficient GPUs** like [A100 GPUs](/concepts/a100-gpu-support-on-databricks.md) for many deep learning tasks. A100 GPUs are available on all clouds and offer high performance for training large language models, natural language processing, object detection, and recommendation engines. ^[best-practices-for-deep-learning-on-databricks-databricks-on-aws.md]
- **Monitor performance** with [TensorBoard](/concepts/tensorboard-on-databricks.md) and cluster metrics to identify bottlenecks such as network, processor, or memory saturation. ^[best-practices-for-deep-learning-on-databricks-databricks-on-aws.md]
- **Optimize batch size and early stopping** to maximize GPU utilization. Adjust batch size and learning rate proportionally, and use early stopping callbacks to avoid wasted computation. ^[best-practices-for-deep-learning-on-databricks-databricks-on-aws.md]

## Related Concepts

- GPU Cluster – General cluster configuration that includes GPU nodes.
- [Distributed Training](/concepts/workload-yaml-for-distributed-training.md) – Scaling training across multiple nodes when single-node performance is insufficient.
- Single Node Cluster – General concept of a driver-only cluster.
- [Deep learning best practices on Databricks](/concepts/deep-learning-best-practices-on-databricks.md) – Broader guidance for deep learning workflows.
- GPU Scheduling – Optimizing GPU utilization for training and inference.
- [Databricks Runtime for Machine Learning](/concepts/databricks-runtime-for-machine-learning.md) – Pre-built runtime that includes GPU drivers and deep learning libraries.

## Sources

- best-practices-for-deep-learning-on-databricks-databricks-on-aws.md

# Citations

1. [best-practices-for-deep-learning-on-databricks-databricks-on-aws.md](/references/best-practices-for-deep-learning-on-databricks-databricks-on-aws-4a84d373.md)
