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Unsloth Studio is a browser UI for running and training local models. This guide uses the official Linux installer on a Thunder Compute base instance, then exposes Studio through Thunder Compute port forwarding.
These commands were verified on a single A100 80 GB development instance. The same setup path is suitable for other GPU instances with enough disk space for the models and datasets you plan to use.

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Create the instance

Create a GPU instance with the base template and enough disk for Studio, models, and datasets: 
tnr create --mode development --gpu a100 --num-gpus 1 --vcpus 12 --template base --disk 250
If A100 is unavailable, use the same shape with an available GPU: 
# RTX A6000
tnr create --mode development --gpu a6000 --num-gpus 1 --vcpus 6 --template base --disk 250

# H100 80 GB
tnr create --mode development --gpu h100 --num-gpus 1 --vcpus 16 --template base --disk 250
Connect to the instance: 
tnr status
tnr connect <instance-id>
Check that the GPU is visible: 
nvidia-smi --query-gpu=name,memory.total,driver_version --format=csv,noheader
Create an A100 instance and verify the GPU

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Install Unsloth Studio

Install the system packages that Unsloth Studio needs to build and run its local inference components: 
sudo apt-get update
sudo apt-get install -y cmake libcurl4-openssl-dev
Run the official Unsloth Studio installer: 
curl -fsSL https://unsloth.ai/install.sh | sh
The installer creates a Python environment under ~/.unsloth/studio and writes an unsloth launcher into ~/.local/bin. For the current SSH session, add the launcher directory to your path: 
export PATH="$HOME/.local/bin:$PATH"
Install Unsloth Studio on a Thunder Compute instance

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Start Studio

Start Unsloth Studio on port 8888 and listen on all interfaces: 
nohup unsloth studio -H 0.0.0.0 -p 8888 > ~/unsloth-studio.log 2>&1 &
Check that Studio is running: 
tail -n 40 ~/unsloth-studio.log
curl -s http://127.0.0.1:8888/api/health | python3 -m json.tool
The health endpoint should return a JSON response with "status":"healthy". Start Unsloth Studio and check the health endpoint
curl -I http://127.0.0.1:8888 may return 405 Method Not Allowed because Studio does not accept HEAD requests on /. Use curl http://127.0.0.1:8888 or /api/health instead.

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Get the admin password

On first launch, Studio creates a default admin user named unsloth. Read the generated bootstrap password from the instance: 
cat ~/.unsloth/studio/auth/.bootstrap_password
Use this password the first time you open Studio, then change it in the UI.

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Open Studio in your browser

From a second terminal on your local machine, forward port 8888: 
tnr ports forward <instance-id> --add 8888
Your Studio URL uses this format: 
https://<instance-uuid>-8888.thundercompute.net
Open the URL in your browser and sign in with:
FieldValue
Usernameunsloth
PasswordThe value from ~/.unsloth/studio/auth/.bootstrap_password
After the bootstrap login, Studio prompts you to choose a new password for future sessions. Unsloth Studio first-run account setup screen

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Stop Studio

Stop the Studio process when you are done using it: 
pkill -f "unsloth studio"
exit
Then delete the instance from your local terminal: 
tnr delete <instance-id>
Billing stops when the instance is deleted.

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Docker Note

Unsloth also publishes an official unsloth/unsloth Docker image. Thunder Compute supports Docker with the --device nvidia.com/gpu=all flag, but Docker support is experimental because the instance itself is already containerized. Use the native installer above for the first Studio setup.

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References