Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.
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The Lambda Labs GPU Cloud skill provides users with dedicated GPU instances for machine learning (ML) training and inference workloads. It offers both reserved and on-demand instances with full SSH access, allowing for flexibility in handling long training jobs and multi-node clusters. This service is designed for individuals and teams looking for high-performance computing power for tasks like training large models or running inference jobs, with the added benefit of persistent storage and simple pricing. It helps users avoid the complexity of managing infrastructure while providing scalable GPU resources for demanding ML tasks.
To launch a GPU instance, visit the Lambda Labs console, select the GPU type and region, choose an SSH key, and optionally attach a filesystem. After confirming your settings, click 'Launch instance' and wait 3-15 minutes for the instance to be ready.
Lambda Labs offers a variety of GPUs including B200, H100, A100, A10, A6000, and V100, each optimized for different workloads like large model training, inference, and production tasks.
Pricing varies depending on the GPU type, ranging from $0.55 per hour for V100 GPUs to $4.99 per hour for B200 SXM GPUs. There are no egress fees, and the pricing is pay-per-minute.
Lambda Labs GPU instances come with Lambda Stack pre-installed, which includes software such as PyTorch, TensorFlow, CUDA, cuDNN, NCCL, JAX, and JupyterLab, as well as the latest NVIDIA drivers and Ubuntu 22.04 LTS.
Yes, Lambda Labs supports 1-Click Clusters with 16-512 GPUs, allowing users to set up high-performance multi-node clusters with InfiniBand for distributed training.
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