This tutorial demonstrates how to use Instant Clusters with Axolotl to fine-tune large language models (LLMs) across multiple GPUs. By leveraging PyTorch’s distributed training capabilities and Runpod’s high-speed networking infrastructure, you can significantly accelerate your training process compared to single-GPU setups. Follow the steps below to deploy a cluster and start training your models efficiently.

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Step 1: Deploy an Instant Cluster

  1. Open the Instant Clusters page on the Runpod web interface.
  2. Click Create Cluster.
  3. Use the UI to name and configure your Cluster. For this walkthrough, keep Pod Count at 2 and select the option for 16x H100 SXM GPUs. Keep the Pod Template at its default setting (Runpod PyTorch).
  4. Click Deploy Cluster. You should be redirected to the Instant Clusters page after a few seconds.

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Step 2: Set up Axolotl on each Pod

  1. Click your cluster to expand the list of Pods.
  2. Click on a Pod, for example CLUSTERNAME-pod-0, to expand the Pod.
  3. Click Connect, then click Web Terminal.
  4. In the terminal that opens, run this command to clone the Axolotl repository into the Pod’s main directory: 
    git clone https://github.com/axolotl-ai-cloud/axolotl
  5. Navigate to the axolotl directory: 
    cd axolotl
  6. Install the required packages: 
    pip3 install -U packaging setuptools wheel ninja
pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'
  7. Navigate to the examples/llama-3 directory: 
    cd examples/llama-3
Repeat these steps for each Pod in your cluster.

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Step 3: Start the training process on each Pod

Run this command in the web terminal of each Pod: 
torchrun \
    --nnodes $NUM_NODES \
    --node_rank $NODE_RANK \
    --nproc_per_node $NUM_TRAINERS \
    --rdzv_id "myjob" \
    --rdzv_backend static \
    --rdzv_endpoint "$PRIMARY_ADDR:$PRIMARY_PORT" -m axolotl.cli.train lora-1b.yml
Currently, the dynamic c10d backend is not supported. Please keep the rdzv_backend flag set to static.
After running the command on the last Pod, you should see output similar to this after the training process is complete: 
...
{'loss': 1.2569, 'grad_norm': 0.11112671345472336, 'learning_rate': 5.418275829936537e-06, 'epoch': 0.9}
{'loss': 1.2091, 'grad_norm': 0.11100614815950394, 'learning_rate': 3.7731999690749585e-06, 'epoch': 0.92}
{'loss': 1.2216, 'grad_norm': 0.10450132936239243, 'learning_rate': 2.420361737256438e-06, 'epoch': 0.93}
{'loss': 1.223, 'grad_norm': 0.10873789340257645, 'learning_rate': 1.3638696597277679e-06, 'epoch': 0.95}
{'loss': 1.2529, 'grad_norm': 0.1063728854060173, 'learning_rate': 6.069322682050516e-07, 'epoch': 0.96}
{'loss': 1.2304, 'grad_norm': 0.10996092110872269, 'learning_rate': 1.518483566683826e-07, 'epoch': 0.98}
{'loss': 1.2334, 'grad_norm': 0.10642101615667343, 'learning_rate': 0.0, 'epoch': 0.99}
{'train_runtime': 61.7602, 'train_samples_per_second': 795.189, 'train_steps_per_second': 1.085, 'train_loss': 1.255359119443751, 'epoch': 0.99}

100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 67/67 [01:00<00:00,  1.11it/s]
[2025-04-01 19:24:22,603] [INFO] [axolotl.train.save_trained_model:211] [PID:1009] [RANK:0] Training completed! Saving pre-trained model to ./outputs/lora-out.
Congrats! You’ve successfully trained a model using Axolotl on an Instant Cluster. Your fine-tuned model has been saved to the ./outputs/lora-out directory. You can now use this model for inference or continue training with different parameters.

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Step 4: Clean up

If you no longer need your cluster, make sure you return to the Instant Clusters page and delete your cluster to avoid incurring extra charges.
You can monitor your cluster usage and spending using the Billing Explorer at the bottom of the Billing page section under the Cluster tab.

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Next steps

Now that you’ve successfully deployed and tested an Axolotl distributed training job on an Instant Cluster, you can:
  • Fine-tune your own models by modifying the configuration files in Axolotl to suit your specific requirements.
  • Scale your training by adjusting the number of Pods in your cluster (and the size of their containers and volumes) to handle larger models or datasets.
  • Try different optimization techniques such as DeepSpeed, FSDP (Fully Sharded Data Parallel), or other distributed training strategies.
For more information on fine-tuning with Axolotl, refer to the Axolotl documentation.