lambda-labs-gpu-cloud

You can provision on-demand GPU cloud instances for training by selecting from H100, B200, A100, and other GPU types, then using API-driven workflows to launch nodes with preinstalled Lambda Stack environments. This provides SSH access and supports multi-node, multi-GPU orchestration.

Yes, distributed GPU training on Lambda Labs supports Slurm and torchrun orchestration. The preinstalled Lambda Stack includes NCCL, FSDP, and DeepSpeed examples, enabling multi-node, multi-GPU training at scale without manual environment configuration.

Persistent filesystems handle checkpointing by allowing you to attach Lambda filesystems to running instances. This persists datasets and checkpoints across instance terminations, speeding up recovery when you launch new GPU nodes for continued training.

The best way to automate GPU instance lifecycle operations in CI is through API and CLI automation. You can programmatically list instance types, manage SSH keys, launch and terminate nodes, and poll instance readiness to integrate infrastructure provisioning into automated workflows.

Lambda Labs GPU cloud supports batch inference and model fine-tuning workflows through dedicated GPU instances. You can right-size single- or multi-GPU nodes with persistent filesystem attachment for development, production training, and inference tasks.

Persistent filesystems are used instead of local storage because they attach to Lambda filesystems and persist datasets across instance lifecycles. This prevents data loss during node terminations and accelerates training recovery by maintaining continuous access to checkpoints.