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 clust
Install
Use withClaudeCursorCodex
The file stays in the publisher's repository. PlugMCP does not host or rewrite it.
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.
name: lambda-labs-gpu-cloud
description: 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.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Infrastructure, GPU Cloud, Training, Inference, Lambda Labs]
dependencies: [lambda-cloud-client>=1.0.0]
Lambda Labs GPU Cloud
Comprehensive guide to running ML workloads on Lambda Labs GPU cloud with on-demand instances and 1-Click Clusters.
# Get instance IP from console
ssh ubuntu@<INSTANCE-IP>
# Or with specific key
ssh -i ~/.ssh/lambda_key ubuntu@<INSTANCE-IP>
GPU instances
Available GPUs
GPU
VRAM
Price/GPU/hr
Best For
B200 SXM6
180 GB
$4.99
Largest models, fastest training
H100 SXM
80 GB
$2.99-3.29
Large model training
H100 PCIe
80 GB
$2.49
Cost-effective H100
GH200
96 GB
$1.49
Single-GPU large models
A100 80GB
80 GB
$1.79
Production training
A100 40GB
40 GB
$1.29
Standard training
A10
24 GB
$0.75
Inference, fine-tuning
A6000
48 GB
$0.80
Good VRAM/price ratio
V100
16 GB
$0.55
Budget training
Instance configurations
8x GPU: Best for distributed training (DDP, FSDP)
4x GPU: Large models, multi-GPU training
2x GPU: Medium workloads
1x GPU: Fine-tuning, inference, development
Launch times
Single-GPU: 3-5 minutes
Multi-GPU: 10-15 minutes
Lambda Stack
All instances come with Lambda Stack pre-installed:
# Check GPU
nvidia-smi
# Check PyTorch
python -c "import torch; print(torch.cuda.is_available())"
# Check CUDA version
nvcc --version
Python API
Installation
pip install lambda-cloud-client
Authentication
import os
import lambda_cloud_client
# Configure with API key
configuration = lambda_cloud_client.Configuration(
host="https://cloud.lambdalabs.com/api/v1",
access_token=os.environ["LAMBDA_API_KEY"]
)
List available instances
with lambda_cloud_client.ApiClient(configuration) as api_client:
api = lambda_cloud_client.DefaultApi(api_client)
# Get available instance types
types = api.instance_types()
for name, info in types.data.items():
print(f"{name}: {info.instance_type.description}")
# On instance
jupyter lab --ip=0.0.0.0 --port=8888
# From local machine with tunnel
ssh -L 8888:localhost:8888 ubuntu@<IP>
# Open http://localhost:8888
Download 09-infrastructure/lambda-labs/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does lambda-labs-gpu-cloud live?
The publisher keeps it at https://github.com/Orchestra-Research/AI-Research-SKILLs.
Which agents can use lambda-labs-gpu-cloud?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.