Lambda GPU Cloud for Deep Learning

Train the most demanding AI, ML, and Deep Learning models. Scale from a single machine to an entire fleet of VMs with a few clicks.

Training with Lambda

Anthem
Carnegie Mellon
Los Alamos National Lab
MIT
Laptop with deep learning notebook connecting to a cloud GPU server.

Supporting research at every stage

Start or scale up your Deep Learning project with Lambda Cloud. Get started quickly, save on compute costs, and easily scale to hundreds of GPUs.

Discover more

Start training models right away

  • Major frameworks preinstalled

    Every VM comes preinstalled with the latest version of Lambda Stack — which includes major deep learning frameworks and CUDA® drivers.

  • Jupyter notebooks

    In seconds, access a dedicated Jupyter Notebook development environment for each machine directly from the cloud dashboard.

  • Direct SSH access

    For direct access, connect via the Web Terminal in the dashboard or use SSH directly with one of your provided SSH keys.

TensorFlow
Jupyter
PyTorch
Keras
Caffe
Ubuntu

Spend less

Lower compute costs, without commitments

  • Save up to 50% on compute costs

    By building compute infrastructure at scale for the unique requirements of deep learning researchers, Lambda can pass on significant savings.

  • Reduce cloud TCO

    Benefit from the flexibility of using cloud computing without paying a fortune in on-demand pricing when workloads rapidly increase.

  • No multi-year commitments

    We provide on-demand pricing at or below typical one year commitment pricing — you benefit from lower costs without lock-in to a specific instance type.

Column chart showing Lambda GPU instance at 1.50 an hour vs the competitors at 1.95 and 3.06 an hour.

Scale up

Seamlessly scale GPU compute infrastructure

  • Multi-node distributed training

    Instances support up to 10 Gbps of inter-node bandwidth to enable distributed training with Horovod or another framework.

  • Hyper-parameter optimization

    Reduce model optimization times by scaling across large numbers of GPUs on one or more instances.

  • Growing research teams

    Supplement oversubscribed or underpowered compute resources with dedicated instances for new team members.

Stylized UI showing the ability to spin up many GPU instances.