GPU Instances | NVIDIA Brev Documentation

Brev instances are GPU-equipped virtual machines preconfigured for AI/ML development. Each instance comes with Python, CUDA, Docker, and Jupyter preinstalled.

What is a GPU Instance?

A GPU instance is a cloud virtual machine with one or more NVIDIA GPUs attached. Brev abstracts away the complexity of cloud setup, giving you instant access to configured development environments across multiple cloud providers.

Included with every instance

NVIDIA GPU(s) with CUDA drivers
Python 3.10+ with pip
Docker and Docker Compose
JupyterLab
SSH access

Brev supports a wide range of NVIDIA GPUs. Refer to GPU Types for the complete catalog with specifications and recommended use cases.

Instance Lifecycle

Instances move through several states during their lifetime. Understanding these states helps you manage costs and data.

Create → Running ⇄ Stopped → Deleted

Running

The instance is active and accessible. Brev bills you per hour for compute time. Connect with SSH, shell, or VS Code.

Stopped

When you stop an instance, Brev releases the GPU back to the cloud provider while preserving your data. You avoid compute charges, but your data remains bound to the original provider and region.

What happens when you stop:

Brev releases the GPU to the provider’s pool
Your data in /home/ubuntu/workspace persists on the provider’s storage
No compute charges while stopped (minimal storage costs apply)

Restarting a stopped instance:

Brev attempts to provision the same GPU type in the same provider and region
If capacity is unavailable, the restart fails, and your data remains inaccessible
You must wait for capacity or delete the instance (losing data)

Use brev start to resume a stopped instance.

Capacity risk: GPU availability varies by provider and region. Popular GPU types frequently hit capacity limits. If you stop an instance and capacity becomes unavailable, you cannot access your data until capacity returns. Push important work to Git before stopping.

When to Stop, When to Delete

Consider your situation before choosing:

Scenario	Recommendation	Why
Short break (hours)	Stop	Likely to get same capacity back.
Overnight or weekend	Stop with caution	Push work to Git first; capacity may change.
Extended break (days or more)	Delete	Avoid storage costs and capacity lock-in.
Switching GPU types	Delete	Stopped instances cannot change GPU type.
Need maximum flexibility	Delete	No provider or region constraints on next launch.

Deleted

Brev permanently deletes (removes) the instance and all data. You cannot undo this action.

Data Persistence

Understanding where data persists helps you avoid losing work.

Location	Persists on Stop?	Persists on Delete?
`/home/ubuntu/workspace`	Yes	No
`/tmp`	No	No
System packages	Yes	No
Docker images/containers	Yes	No

Best practice: Store all your work in /home/ubuntu/workspace and push to a Git remote regularly. Use Docker volumes for persistent container data.

Billing

Running: Brev bills per hour based on GPU type
Stopped: No compute charges (minimal storage costs apply)
Deleted: No charges