lambda-labs-gpu-cloud

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

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

Launch via console

1. Go to https://cloud.lambda.ai/instances
2. Click "Launch instance"
3. Select GPU type and region
4. Choose SSH key
5. Optionally attach filesystem
6. 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

Details, code examples and parameter tables: references/python-api.md. Read it when this step applies.

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

1. Go to Storage in Lambda console
2. Click "Create filesystem"
3. Select region (must match instance region)
4. 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

1. Go to Instances page
2. Click "Launch" in Cloud IDE column
3. 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

1. Use filesystems: Avoid re-downloading data
2. Checkpoint frequently: Resume interrupted training
3. Right-size: Don't over-provision GPUs
4. 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

Related skills

• modal-serverless-gpu: Serverless GPU cloud platform for running ML workloads.
• skypilot-multi-cloud-orchestration: Multi-cloud orchestration for ML workloads with automatic cost optimization.
• modal: Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs.