lambda-labs-gpu-cloud

To provision on-demand GPU instances for ML training, you use the Skill to launch hardware like H100 or A100 in global regions, attach persistent filesystems, and gain dedicated SSH access to start running workloads.

Yes, you can set up multi-node GPU clusters for large-scale machine learning using 1-Click Clusters, which provide Slurm-managed environments scaling up to 512 GPUs for distributed training workloads.

Yes, you need a valid Lambda Labs API key and the lambda-cloud-client Python package installed to authenticate and launch GPU cloud instances for your machine learning training and inference.

The best way to persist datasets and checkpoints across instance restarts is to attach persistent filesystems to your GPU cloud instances, ensuring your data survives machine restarts and stops.

Yes, PyTorch and TensorFlow are pre-installed via Lambda Stack, alongside CUDA, NCCL, and JupyterLab, providing a ready-to-use machine learning environment immediately upon SSH access.

GPU cloud provisioning supports pay-per-minute pricing with no egress fees across dozens of regions, allowing cost-effective scaling for machine learning training and inference workloads.