lambda-labs-gpu-cloud

Rents dedicated GPU machines by the minute, from a single card up to a multi-hundred-GPU cluster, with full SSH access, a pre-installed ML software stack, and storage that survives a restart.

WHEN TO USE

SCOPE

USE FOR

Launch a single GPU instance over SSH to run a multi-day model training job.
Spin up a 16-to-512-GPU Slurm cluster for large-scale distributed training.
Keep training checkpoints and datasets on a filesystem that survives instance restarts.
Run cost-effective batch inference on a smaller, cheaper GPU instance.

NOT FOR

Wanting serverless, auto-scaling GPU workloads — that's Modal's niche, not dedicated instances.
Needing multi-cloud orchestration or automatic cost optimization — SkyPilot handles that.
Chasing the absolute cheapest spot pricing — RunPod or Vast.ai fit that better.

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In this document (18)

Lambda Labs GPU Cloud

Comprehensive guide to running ML workloads on Lambda Labs GPU cloud with on-demand instances and 1-Click Clusters.

When to use Lambda Labs

Use Lambda Labs when:

Need dedicated GPU instances with full SSH access
Running long training jobs (hours to days)
Want simple pricing with no egress fees
Need persistent storage across sessions
Require high-performance multi-node clusters (16-512 GPUs)
Want pre-installed ML stack (Lambda Stack with PyTorch, CUDA, NCCL)

Key features:

GPU variety: B200, H100, GH200, A100, A10, A6000, V100
Lambda Stack: Pre-installed PyTorch, TensorFlow, CUDA, cuDNN, NCCL
Persistent filesystems: Keep data across instance restarts
1-Click Clusters: 16-512 GPU Slurm clusters with InfiniBand
Simple pricing: Pay-per-minute, no egress fees
Global regions: 12+ regions worldwide

Use alternatives instead:

Modal: For serverless, auto-scaling workloads
SkyPilot: For multi-cloud orchestration and cost optimization
RunPod: For cheaper spot instances and serverless endpoints
Vast.ai: For GPU marketplace with lowest prices

Quick start

Account setup

Create account at https://lambda.ai
Add payment method
Generate API key from dashboard
Add SSH key (required before launching instances)

Launch via console

Go to https://cloud.lambda.ai/instances
Click "Launch instance"
Select GPU type and region
Choose SSH key
Optionally attach filesystem
Launch and wait 3-15 minutes

Connect via SSH

# 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:

# Included software
- Ubuntu 22.04 LTS
- NVIDIA drivers (latest)
- CUDA 12.x
- cuDNN 8.x
- NCCL (for multi-GPU)
- PyTorch (latest)
- TensorFlow (latest)
- JAX
- JupyterLab

Verify installation

# 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}")

Launch instance

from lambda_cloud_client.models import LaunchInstanceRequest

request = LaunchInstanceRequest(
    region_name="us-west-1",
    instance_type_name="gpu_1x_h100_sxm5",
    ssh_key_names=["my-ssh-key"],
    file_system_names=["my-filesystem"],  # Optional
    name="training-job"
)

response = api.launch_instance(request)
instance_id = response.data.instance_ids[0]
print(f"Launched: {instance_id}")

List running instances

instances = api.list_instances()
for instance in instances.data:
    print(f"{instance.name}: {instance.ip} ({instance.status})")

Terminate instance

from lambda_cloud_client.models import TerminateInstanceRequest

request = TerminateInstanceRequest(
    instance_ids=[instance_id]
)
api.terminate_instance(request)

SSH key management

from lambda_cloud_client.models import AddSshKeyRequest

# Add SSH key
request = AddSshKeyRequest(
    name="my-key",
    public_key="ssh-rsa AAAA..."
)
api.add_ssh_key(request)

# List keys
keys = api.list_ssh_keys()

# Delete key
api.delete_ssh_key(key_id)

CLI with curl

List instance types

curl -u $LAMBDA_API_KEY: \
  https://cloud.lambdalabs.com/api/v1/instance-types | jq

Launch instance

curl -u $LAMBDA_API_KEY: \
  -X POST https://cloud.lambdalabs.com/api/v1/instance-operations/launch \
  -H "Content-Type: application/json" \
  -d '{
    "region_name": "us-west-1",
    "instance_type_name": "gpu_1x_h100_sxm5",
    "ssh_key_names": ["my-key"]
  }' | jq

Terminate instance

curl -u $LAMBDA_API_KEY: \
  -X POST https://cloud.lambdalabs.com/api/v1/instance-operations/terminate \
  -H "Content-Type: application/json" \
  -d '{"instance_ids": ["<INSTANCE-ID>"]}' | jq

Persistent storage

Filesystems

Filesystems persist data across instance restarts:

# Mount location
/lambda/nfs/<FILESYSTEM_NAME>

# Example: save checkpoints
python train.py --checkpoint-dir /lambda/nfs/my-storage/checkpoints

Create filesystem

Go to Storage in Lambda console
Click "Create filesystem"
Select region (must match instance region)
Name and create

Attach to instance

Filesystems must be attached at instance launch time:

Via console: Select filesystem when launching
Via API: Include file_system_names in launch request

Best practices

# Store on filesystem (persists)
/lambda/nfs/storage/
  ├── datasets/
  ├── checkpoints/
  ├── models/
  └── outputs/

# Local SSD (faster, ephemeral)
/home/ubuntu/
  └── working/  # Temporary files

SSH configuration

Add SSH key

# Generate key locally
ssh-keygen -t ed25519 -f ~/.ssh/lambda_key

# Add public key to Lambda console
# Or via API

Multiple keys

# On instance, add more keys
echo 'ssh-rsa AAAA...' >> ~/.ssh/authorized_keys

Import from GitHub

# On instance
ssh-import-id gh:username

SSH tunneling

# Forward Jupyter
ssh -L 8888:localhost:8888 ubuntu@<IP>

# Forward TensorBoard
ssh -L 6006:localhost:6006 ubuntu@<IP>

# Multiple ports
ssh -L 8888:localhost:8888 -L 6006:localhost:6006 ubuntu@<IP>

JupyterLab

Launch from console

Go to Instances page
Click "Launch" in Cloud IDE column
JupyterLab opens in browser

Manual access

# 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

Training workflows

Single-GPU training

# SSH to instance
ssh ubuntu@<IP>

# Clone repo
git clone https://github.com/user/project
cd project

# Install dependencies
pip install -r requirements.txt

# Train
python train.py --epochs 100 --checkpoint-dir /lambda/nfs/storage/checkpoints

Multi-GPU training (single node)

# train_ddp.py
import torch
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP

def main():
    dist.init_process_group("nccl")
    rank = dist.get_rank()
    device = rank % torch.cuda.device_count()

    model = MyModel().to(device)
    model = DDP(model, device_ids=[device])

    # Training loop...

if __name__ == "__main__":
    main()

# Launch with torchrun (8 GPUs)
torchrun --nproc_per_node=8 train_ddp.py

Checkpoint to filesystem

import os

checkpoint_dir = "/lambda/nfs/my-storage/checkpoints"
os.makedirs(checkpoint_dir, exist_ok=True)

# Save checkpoint
torch.save({
    'epoch': epoch,
    'model_state_dict': model.state_dict(),
    'optimizer_state_dict': optimizer.state_dict(),
    'loss': loss,
}, f"{checkpoint_dir}/checkpoint_{epoch}.pt")

1-Click Clusters

Overview

High-performance Slurm clusters with:

16-512 NVIDIA H100 or B200 GPUs
NVIDIA Quantum-2 400 Gb/s InfiniBand
GPUDirect RDMA at 3200 Gb/s
Pre-installed distributed ML stack

Included software

Ubuntu 22.04 LTS + Lambda Stack
NCCL, Open MPI
PyTorch with DDP and FSDP
TensorFlow
OFED drivers

Storage

24 TB NVMe per compute node (ephemeral)
Lambda filesystems for persistent data

Multi-node training

# On Slurm cluster
srun --nodes=4 --ntasks-per-node=8 --gpus-per-node=8 \
  torchrun --nnodes=4 --nproc_per_node=8 \
  --rdzv_backend=c10d --rdzv_endpoint=$MASTER_ADDR:29500 \
  train.py

Networking

Bandwidth

Inter-instance (same region): up to 200 Gbps
Internet outbound: 20 Gbps max

Firewall

Default: Only port 22 (SSH) open
Configure additional ports in Lambda console
ICMP traffic allowed by default

Private IPs

# Find private IP
ip addr show | grep 'inet '

Common workflows

Workflow 1: Fine-tuning LLM

# 1. Launch 8x H100 instance with filesystem

# 2. SSH and setup
ssh ubuntu@<IP>
pip install transformers accelerate peft

# 3. Download model to filesystem
python -c "
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf')
model.save_pretrained('/lambda/nfs/storage/models/llama-2-7b')
"

# 4. Fine-tune with checkpoints on filesystem
accelerate launch --num_processes 8 train.py \
  --model_path /lambda/nfs/storage/models/llama-2-7b \
  --output_dir /lambda/nfs/storage/outputs \
  --checkpoint_dir /lambda/nfs/storage/checkpoints

Workflow 2: Batch inference

# 1. Launch A10 instance (cost-effective for inference)

# 2. Run inference
python inference.py \
  --model /lambda/nfs/storage/models/fine-tuned \
  --input /lambda/nfs/storage/data/inputs.jsonl \
  --output /lambda/nfs/storage/data/outputs.jsonl

Cost optimization

Choose right GPU

Task	Recommended GPU
LLM fine-tuning (7B)	A100 40GB
LLM fine-tuning (70B)	8x H100
Inference	A10, A6000
Development	V100, A10
Maximum performance	B200

Reduce costs

Use filesystems: Avoid re-downloading data
Checkpoint frequently: Resume interrupted training
Right-size: Don't over-provision GPUs
Terminate idle: No auto-stop, manually terminate

Monitor usage

Dashboard shows real-time GPU utilization
API for programmatic monitoring

Common issues

Issue	Solution
Instance won't launch	Check region availability, try different GPU
SSH connection refused	Wait for instance to initialize (3-15 min)
Data lost after terminate	Use persistent filesystems
Slow data transfer	Use filesystem in same region
GPU not detected	Reboot instance, check drivers

References

Advanced Usage - Multi-node training, API automation
Troubleshooting - Common issues and solutions

Resources

Documentation: https://docs.lambda.ai
Console: https://cloud.lambda.ai
Pricing: https://lambda.ai/instances
Support: https://support.lambdalabs.com
Blog: https://lambda.ai/blog

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What's inside

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View on GitHub

Lambda Labs GPU Cloud

When to use Lambda Labs

Quick start

Account setup

Launch via console

Connect via SSH

GPU instances

Available GPUs

Instance configurations

Launch times

Lambda Stack

Verify installation

Python API

Installation

Authentication

List available instances

Launch instance

List running instances

Terminate instance

SSH key management

CLI with curl

List instance types

Launch instance

Terminate instance

Persistent storage

Filesystems

Create filesystem

Attach to instance

Best practices

SSH configuration

Add SSH key

Multiple keys

Import from GitHub

SSH tunneling

JupyterLab

Launch from console

Manual access

Training workflows

Single-GPU training

Multi-GPU training (single node)

Checkpoint to filesystem

1-Click Clusters

Overview

Included software

Storage

Multi-node training

Networking

Bandwidth

Firewall

Private IPs

Common workflows

Workflow 1: Fine-tuning LLM

Workflow 2: Batch inference

Cost optimization

Choose right GPU

Reduce costs

Monitor usage

Common issues

References

Resources

Account or credentials required

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