What GPU types can I manage with this skill?

This skill supports the full range of Lambda Labs offerings, including NVIDIA B200, H100, GH200, A100, A10, A6000, and V100 instances.

Is the machine learning software stack pre-installed?

Yes, all managed instances come with the Lambda Stack, which includes Ubuntu, NVIDIA drivers, CUDA, cuDNN, NCCL, PyTorch, and TensorFlow.

How does this skill handle persistent data storage?

The skill provides instructions for creating and attaching persistent filesystems that allow you to keep datasets and model checkpoints even after instances are terminated.

Does this skill support multi-node distributed training?

Yes, it includes specific patterns for 1-Click Clusters (16-512 GPUs) using Slurm, InfiniBand, and GPUDirect RDMA.

Lambda Labs GPU Cloud

Name: Lambda Labs GPU Cloud
Author: zechenzhangAGI

byzechenzhangAGI

•

384

Cloud Infrastructure

Provision and manage high-performance GPU instances on Lambda Labs for machine learning training and inference workloads.

About

This skill empowers developers and AI researchers to leverage Lambda Labs' specialized GPU infrastructure directly through Claude. It provides comprehensive patterns for launching on-demand instances—ranging from NVIDIA B200s to A10s—managing persistent filesystems, and orchestrating high-performance 1-Click Clusters with InfiniBand. Whether you are fine-tuning LLMs, running distributed training via Slurm, or setting up cost-effective inference endpoints, this skill simplifies the deployment and management of specialized ML hardware and the pre-configured Lambda Stack.

Key Features

Management of persistent network filesystems for durable dataset and checkpoint storage
Automated provisioning of high-end NVIDIA GPUs including H100, B200, and A100
Integration with the Lambda Cloud Python API and CLI for programmatic infrastructure control
Orchestration of multi-node 1-Click Clusters with Slurm and InfiniBand support
384 GitHub stars
Guidance for utilizing the pre-installed Lambda Stack with CUDA, PyTorch, and TensorFlow

Use Cases

Fine-tuning large language models on dedicated multi-GPU H100 instances
Deploying cost-optimized batch inference pipelines using A10 or A6000 GPUs
Scaling large-scale distributed training jobs across InfiniBand-connected clusters

About

Key Features

Management of persistent network filesystems for durable dataset and checkpoint storage
Automated provisioning of high-end NVIDIA GPUs including H100, B200, and A100
Integration with the Lambda Cloud Python API and CLI for programmatic infrastructure control
Orchestration of multi-node 1-Click Clusters with Slurm and InfiniBand support
384 GitHub stars
Guidance for utilizing the pre-installed Lambda Stack with CUDA, PyTorch, and TensorFlow

Use Cases

Fine-tuning large language models on dedicated multi-GPU H100 instances
Deploying cost-optimized batch inference pipelines using A10 or A6000 GPUs
Scaling large-scale distributed training jobs across InfiniBand-connected clusters