lambda-labs-gpu-cloud

To launch on-demand GPU cloud instances for ML training, you can provision machines featuring simple SSH access and pre-installed ML stacks like PyTorch and TensorFlow. This allows you to quickly access NVIDIA GPUs for large-scale workloads.

Yes, you can use persistent filesystems to store data and model checkpoints across instance restarts. This ensures your training progress and datasets remain intact on the attached storage even when you terminate your GPU cloud instances.

Yes, the GPU cloud supports deploying multi-node clusters for distributed training. You can utilize 1-click clusters to quickly deploy large-scale Slurm clusters designed to handle extensive machine learning training workloads across multiple high-end NVIDIA GPUs.

A wide variety of NVIDIA GPUs are available for machine learning inference, including B200, H100, and A100 models. These instances come with pre-installed ML stacks to support both large-scale training and inference workloads.

No, you do not need to manually install PyTorch and TensorFlow on your cloud GPU instances. The instances come equipped with pre-installed ML stacks, allowing you to immediately start your machine learning training and inference tasks upon SSH access.

The best way to train a large language model requiring multiple high-end GPUs is to quickly provision an 8x H100 cluster. You can run your training for several days and securely store the resulting checkpoints on a persistent filesystem.