Lambda Labs

Lambda Labs provides on-demand GPU cloud instances and GPU workstations for AI training and research, with a deliberately simpler user experience than the major hyperscalers. Every instance comes pre-installed with the Lambda Stack — a curated, tested combination of CUDA, PyTorch, TensorFlow, and common ML libraries.

• On-demand GPU instances — launch A100, H100, and multi-GPU instances from a simple dashboard or REST API, ready to use in minutes
• Pre-configured environment — every instance ships with the Lambda Stack (CUDA, cuDNN, PyTorch, TensorFlow, Jupyter) pre-installed and tested for compatibility
• GPU workstations — purchase dedicated Lambda Scalar, Vector, and Hyperplane servers for on-premise GPU compute
• Multi-GPU clusters — reserve 8-GPU and multi-node clusters for distributed training with InfiniBand networking
• Research-friendly — straightforward pricing, SSH access, and no complex cloud abstractions or IAM configurations

AI researchers, ML engineers at startups, and university research labs choose Lambda because it eliminates the cloud complexity tax. You SSH into a machine, your stack is ready, and you start training — no Kubernetes, no IAM roles, no storage configuration required.

Lambda is particularly popular with teams that rotate between local workstations (for prototyping) and cloud instances (for full training runs), since the Lambda Stack provides a consistent environment across both.

1. Create an account — sign up at lambdalabs.com/cloud and add a payment method (credit card or invoice for teams).
2. Launch an instance — from the dashboard, select a GPU type and region, then click Launch. Available instance types include 1x A100, 8x A100, 1x H100, and 8x H100 configurations.
3. SSH into your instance:

   ssh ubuntu@your-instance-ip

   The Lambda Stack is pre-installed — verify with:

   python3 -c "import torch; print(torch.cuda.is_available())"  # True
   nvidia-smi  # Shows your GPU(s)

4. Start training — your instance is ready for PyTorch, TensorFlow, or JAX training immediately. Upload data via scp or mount persistent storage.
5. Use the API — automate instance management programmatically:

   curl -X POST https://cloud.lambdalabs.com/api/v1/instance-operations/launch \
     -H 'Authorization: Bearer YOUR_API_KEY' \
     -d '{"region_name": "us-east-1", "instance_type_name": "gpu_1x_a100"}'

Pricing: 1x A100 (40GB): ~$1.10/hr. 8x A100 (80GB): ~$8.80/hr. 1x H100: ~$2.00/hr. 8x H100: ~$16.00/hr. No minimum commitment. Full pricing details.