> ## Documentation Index > Fetch the complete documentation index at: https://docs.together.ai/llms.txt > Use this file to discover all available pages before exploring further. # Quickstart > Create a reserved or on-demand GPU cluster and connect to it. ## Create a cluster Follow these steps to create your first GPU cluster: ### 1. Access the cluster console 1. Log into [api.together.ai](https://api.together.ai) 2. Select **GPU Clusters** in the top navigation menu 3. Select **Create Cluster** ### 2. Choose capacity type Select the billing mode that fits your needs: * **Reserved:** Pay upfront to reserve capacity for 1-90 days with discounted pricing. * **On-demand:** Pay hourly with no commitment. Terminate anytime. [Learn more about capacity types →](/docs/gpu-clusters-overview#capacity-options) ### 3. Configure your cluster **Cluster size** * Select the number and type of GPUs (for example, `8xH100`). * Available options: H100, H200, and B200. **Cluster name** * Enter a descriptive name. **Cluster type** * **Kubernetes:** For containerized workloads and Kubernetes-native tools. * **Slurm:** For HPC-style batch scheduling and traditional workflows. **Region** * Defaults to **Any region**. Together assigns the region with the most available capacity for your selected GPU type when you create the cluster. * Select a specific datacenter region instead if you need the cluster in a particular location. * Changing the GPU type resets the region to **Any region** and clears any selected shared volume, because volumes are region-specific. **Duration** (reserved only) * Choose reservation length: 1-90 days. **Shared volume** * Create and name your persistent storage volume. * Minimum size: 1 TiB. * Can be resized later as needed. **Optional settings** * Select NVIDIA driver version. * Select CUDA version. ### 4. Create and verify 1. Select **Proceed** to create your cluster. 2. Monitor the cluster status in the UI as it provisions. 3. Wait for status to transition to **Ready**. Your cluster is now ready to use! ## Connect to the cluster ### For Kubernetes clusters 1. **Install kubectl** * [macOS installation guide](https://kubernetes.io/docs/tasks/tools/install-kubectl-macos/). * Or use your preferred method for your OS. 2. **Download kubeconfig** Use the [Together CLI](/reference/cli/clusters) to download the cluster's credentials to your local `~/.kube/config`. Find your cluster ID with `tg beta clusters list`: ```bash theme={null} tg beta clusters get-credentials --set-default-context ``` 3. **Verify connectivity** ```bash theme={null} kubectl get nodes ``` You should see all worker and control plane nodes listed. 4. **Start using your cluster** * [Deploy workloads](/docs/gpu-clusters-management#kubernetes-usage). * [Access the Kubernetes dashboard](/docs/gpu-clusters-management#kubernetes-dashboard). ### For Slurm clusters 1. **Choose an SSH access method** * On clusters with OIDC enabled, select **OIDC** or **Key-based** in **SSH access method** on the cluster details page. * **OIDC:** Install the [Together CLI](/reference/cli/getting-started) (`uv tool install "together[cli]"`, requires CLI 2.20+ and Python 3.10+) and choose a login name when prompted. No SSH key is required. * **Key-based:** Add your SSH key at [api.together.ai/settings/ssh-key](https://api.together.ai/settings/ssh-key) before cluster creation. 2. **Connect via SSH** * Copy the head node command from the cluster sidebar with **Copy head node SSH command**. * Paste and run the command in your terminal to reach the Slurm login node. 3. **Verify Slurm** ```bash theme={null} sinfo # View node status squeue # View job queue ``` 4. **Start submitting jobs** * [Learn about Slurm commands](/docs/slurm). * Submit batch jobs with `sbatch`. * Run interactive jobs with `srun`. 5. **Optional: Download kubeconfig** Slurm clusters run on Kubernetes, so if you need `kubectl` access to the underlying cluster API, any project member can download the kubeconfig from the cluster details page or with the Together CLI: ```bash theme={null} tg beta clusters get-credentials --set-default-context ``` SSH to the login node remains the primary workflow for submitting jobs. See [Download cluster kubeconfig](/docs/gpu-clusters-management#download-cluster-kubeconfig) for console steps and OIDC visibility rules. ## Common first tasks ### Upload data For small datasets: ```bash theme={null} # Create a pod with your shared volume mounted # Then copy files directly kubectl cp local_file.tar.gz pod-name:/mnt/shared/ ``` For large datasets, create a pod that downloads from S3 or your data source. ### Run a test job **Kubernetes example:** ```bash theme={null} kubectl run test --image=ubuntu --command -- sleep infinity kubectl exec -it test -- bash ``` **Slurm example:** ```bash theme={null} srun --gpus=1 --pty bash nvidia-smi ``` ## Troubleshooting ### Can't see my nodes * Check cluster status in the UI (should be **Ready**). * Re-download the latest credentials with `tg beta clusters get-credentials `. ### SSH connection refused * Verify your SSH key was added before cluster creation. * Check the connection command in the cluster UI. * Ensure you're using the correct hostname. ### Capacity unavailable * Use the **Notify Me** option to get alerts when capacity is available. * Try a different region. * Contact [support@together.ai](mailto:support@together.ai) for custom requirements. ## Next steps Deploy workloads, manage storage, and scale a running cluster. Compare reserved and on-demand capacity and how each is billed. Create and manage clusters from the API or Together CLI. View current reserved and on-demand GPU cluster rates. This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.