Unsloth on DGX Spark

Step 1
Verify prerequisites

Confirm your NVIDIA Spark device has the required CUDA toolkit and GPU resources available.

nvcc --version

The output should show CUDA 13.0.

nvidia-smi

The output should show a summary of GPU information.

Step 2
Get the container image

docker pull nvcr.io/nvidia/pytorch:25.11-py3

Step 3
Launch Docker

docker run --gpus all --ulimit memlock=-1 -it --ulimit stack=67108864 --entrypoint /usr/bin/bash --rm nvcr.io/nvidia/pytorch:25.11-py3

Step 4
Install dependencies inside Docker

pip install transformers peft hf_transfer "datasets==4.3.0" "trl==0.26.1"
pip install --no-deps unsloth unsloth_zoo bitsandbytes

Step 5
Create Python test script

Curl the test script here into the container.

curl -O https://raw.githubusercontent.com/NVIDIA/dgx-spark-playbooks/refs/heads/main/nvidia/unsloth/assets/test_unsloth.py

We will use this test script to validate the installation with a simple fine-tuning task.

Step 6
Run the validation test

Execute the test script to verify Unsloth is working correctly.

python test_unsloth.py

Expected output in the terminal window:

"Unsloth: Will patch your computer to enable 2x faster free finetuning"
Training progress bars showing loss decreasing over 60 steps
Final training metrics showing completion

Step 7
Next steps

Test with your own model and dataset by updating the test_unsloth.py file:

# Replace line 32 with your model choice
model_name = "unsloth/Meta-Llama-3.1-8B-bnb-4bit"

# Load your custom dataset in line 8
dataset = load_dataset("your_dataset_name")

# Adjust training parameter args at line 61
per_device_train_batch_size = 4
max_steps = 1000

Visit https://github.com/unslothai/unsloth/wiki for advanced usage instructions, including:

Unsloth on DGX Spark

Step 1Verify prerequisites

Step 2Get the container image

Step 3Launch Docker

Step 4Install dependencies inside Docker

Step 5Create Python test script

Step 6Run the validation test

Step 7Next steps

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