lambda-labs-gpu-cloud

To provision a Lambda Labs GPU instance for distributed training, use API-driven instance selection to launch reserved machines, attach persistent storage, and configure key-based SSH access for multi-node Slurm clusters.

Yes, you can run multi-node Slurm clusters on Lambda Labs for large-scale inference by provisioning multi-GPU instances, configuring port tunneling, and managing filesystems to support distributed workloads like DDP and FSDP.

The best way to set up persistent storage on a GPU cloud instance is to attach a persistent filesystem during API-driven provisioning, ensuring your training checkpoints and data survive instance teardowns.

Yes, you need SSH tunneling to access JupyterLab on a remote GPU machine, allowing you to securely route local ports to the Lambda Labs instance and manage your ML workloads through a browser interface.

Lambda Labs supports fine-tuning LLMs on H100 GPUs with DeepSpeed by provisioning reserved multi-GPU instances, attaching persistent storage for checkpoints, and utilizing Slurm-based clusters for distributed training.

Your SSH connection may drop during long-running GPU training jobs due to network timeouts or port tunneling misconfigurations, requiring key-based authentication troubleshooting and persistent filesystem checks to ensure reliable remote execution.