Is Lambda Labs Down? How to Check Lambda Labs GPU Status in Real-Time

Quick Answer: To check if Lambda Labs is down, visit apistatuscheck.com/api/lambda-labs for real-time monitoring, or check status.lambdalabs.com for official updates. Common signs include instance launch failures, SSH connection timeouts, GPU availability showing "Out of Stock," API errors, and storage/filesystem mount issues.

When your multi-day ML training job suddenly stops responding or you can't launch that critical A100 instance for your research deadline, every minute matters. Lambda Labs has become a go-to GPU cloud provider for AI researchers, ML engineers, and startups training large models. With high-demand GPUs like H100s and A100s frequently in short supply, distinguishing between a genuine outage and normal capacity constraints is crucial for your workflow planning.

How to Check Lambda Labs Status in Real-Time

1. API Status Check (Fastest Method)

The fastest way to verify Lambda Labs' operational status is through apistatuscheck.com/api/lambda-labs. This real-time monitoring service:

• Tests actual API endpoints every 60 seconds
• Monitors GPU availability APIs across all regions
• Tracks SSH connectivity to active instances
• Shows response times and latency trends
• Provides instant alerts when issues are detected
• Historical uptime tracking over 30/60/90 days

Unlike status pages that require manual updates, API Status Check performs active health checks against Lambda Labs' production infrastructure, giving you the most accurate real-time picture of service availability—critical when you're deciding whether to wait for capacity or move to alternatives like RunPod or Modal.

2. Official Lambda Labs Status Page

Lambda Labs maintains status.lambdalabs.com as their official communication channel for service incidents. The page displays:

• Current operational status for cloud services
• Active incidents and investigations
• Scheduled maintenance windows
• Historical incident reports
• Component-specific status (API, Dashboard, Instances, Storage)

Pro tip: Subscribe to status updates via email or RSS feed to receive immediate notifications when incidents occur. For GPU-intensive workloads, knowing about maintenance windows in advance can save you from unexpected training interruptions.

3. Check the Lambda Cloud Dashboard

Visit cloud.lambdalabs.com and monitor for:

• Dashboard loading issues or timeouts
• GPU availability display errors (all showing unavailable)
• Instance list failing to load or showing stale data
• SSH key management timeouts
• Billing/usage data not updating

If the dashboard is slow or unresponsive, this often indicates broader platform issues affecting the API and instance management systems.

4. Test Instance Launch API

For developers with existing Lambda Labs integrations, a quick API health check:

curl -X GET "https://cloud.lambdalabs.com/api/v1/instance-types" \
  -H "Authorization: Bearer YOUR_API_KEY"

Healthy response should return JSON with available instance types and regions. Look for:

• HTTP 5xx errors (500, 502, 503, 504)
• Connection timeouts (no response in 30+ seconds)
• Authentication errors when your API key is valid
• Empty or malformed JSON responses

5. SSH Connectivity Test

If you have running instances, test SSH connectivity:

# Quick connectivity check
ssh -o ConnectTimeout=10 ubuntu@YOUR_INSTANCE_IP "echo 'Connected'"

# Check GPU visibility
ssh ubuntu@YOUR_INSTANCE_IP "nvidia-smi"

What to look for:

• Connection refused or timeout errors
• Successful connection but GPU commands hang
• filesystem errors when accessing /home or storage

During infrastructure issues, SSH may succeed but GPU visibility or storage access can fail.

Common Lambda Labs Issues and How to Identify Them

Instance Launch Failures

Symptoms:

• Launch requests timing out or hanging indefinitely
• "Unable to launch instance" errors in dashboard
• API returning 500 errors on instance creation requests
• Instances stuck in "launching" state for 10+ minutes

What it means: Lambda Labs manages physical GPU servers that need provisioning. Launch failures can indicate:

• Backend orchestration issues
• Disk imaging problems
• Network configuration failures
• Actual outages vs. capacity constraints

How to distinguish from normal "sold out" capacity:

• Capacity issue: API returns 200 with "No capacity available" message
• Outage: API returns 500/502/503 or times out completely

GPU Availability Issues (A100, H100 Shortages)

Context: High-end GPUs are in extreme demand. Seeing "Unavailable" isn't always an outage—it's often genuine capacity constraints.

Signs of a real outage vs. normal scarcity:

Normal Capacity	Actual Outage
Some regions show available	All regions show unavailable
Lower-tier GPUs still available	All GPU types unavailable
Availability API responds normally	API errors or extreme delays
Status page shows "Operational"	Status page shows incidents

Monitoring strategy:

import requests
import time

def check_gpu_availability(api_key):
    headers = {"Authorization": f"Bearer {api_key}"}
    response = requests.get(
        "https://cloud.lambdalabs.com/api/v1/instance-types",
        headers=headers,
        timeout=30
    )
    
    if response.status_code != 200:
        return f"API ERROR: {response.status_code}"
    
    data = response.json()
    available_gpus = [
        f"{gpu['instance_type_name']} in {region}"
        for gpu in data.get('data', {}).values()
        for region in gpu.get('regions_with_capacity_available', [])
    ]
    
    return available_gpus if available_gpus else "No GPUs available"

# Check every 5 minutes
while True:
    result = check_gpu_availability(API_KEY)
    print(f"[{time.strftime('%H:%M:%S')}] {result}")
    time.sleep(300)

SSH Connection Problems

Common SSH errors during Lambda Labs issues:

ssh: connect to host 150.136.x.x port 22: Connection timed out

Possible causes:

• Network infrastructure issues at Lambda Labs
• Instance entered failed state but dashboard shows "running"
• Security group/firewall misconfiguration during maintenance
• Instance crashed but auto-restart failed

Diagnostic steps:

# 1. Check if instance is reachable
ping -c 4 YOUR_INSTANCE_IP

# 2. Check if SSH port is open
nc -zv YOUR_INSTANCE_IP 22

# 3. Try connecting with verbose output
ssh -vvv ubuntu@YOUR_INSTANCE_IP

# 4. Check from Lambda Labs side via API
curl -X GET "https://cloud.lambdalabs.com/api/v1/instances/YOUR_INSTANCE_ID" \
  -H "Authorization: Bearer YOUR_API_KEY"

Workaround during SSH issues: Lambda Labs doesn't provide web-based console access like AWS EC2 Instance Connect. If SSH is down, your options are limited:

• Wait for infrastructure recovery
• Terminate and relaunch (losing ephemeral data)
• Contact Lambda Labs support for direct intervention

Storage and Filesystem Issues

Symptoms:

• /home directory inaccessible or read-only
• Persistent storage not mounting on instance launch
• Input/output error when accessing files
• Training jobs failing with disk write errors

Example errors:

OSError: [Errno 5] Input/output error: '/home/ubuntu/model_checkpoints/checkpoint.pt'

This indicates NFS or block storage issues, common during:

• Storage backend maintenance
• Network issues between compute and storage
• Filesystem corruption after unclean shutdowns

Emergency data recovery:

# Try to copy critical data to local machine
scp -o ConnectTimeout=10 -r ubuntu@INSTANCE_IP:/home/ubuntu/checkpoints ./backup/

# Or compress and copy
ssh ubuntu@INSTANCE_IP "tar czf /tmp/backup.tar.gz /home/ubuntu/important_data"
scp ubuntu@INSTANCE_IP:/tmp/backup.tar.gz ./

API Rate Limiting and Authentication

Rate limit errors:

{
  "error": {
    "message": "Rate limit exceeded. Try again in 60 seconds.",
    "code": "rate_limit_exceeded"
  }
}

Normal vs. outage scenario:

• Normal: You're making too many requests (> 100/min typically)
• Outage: Getting rate limit errors with minimal requests, or inconsistent responses

Authentication failures during outages:

{
  "error": {
    "message": "Internal server error",
    "code": "internal_error"
  }
}

If you're getting 500 errors with valid API keys, especially inconsistently, this suggests backend authentication service issues.

The Real Impact When Lambda Labs Goes Down

Interrupted ML Training Jobs

The nightmare scenario: You're 72 hours into training a large language model on 8x A100s (costing $10-12/hour per GPU = ~$6,900 spent), and the instance becomes unresponsive.

Without proper checkpointing:

• Training progress lost entirely
• Days of compute time wasted
• Research deadlines missed
• Thousands of dollars down the drain

Cost impact example:

GPU Type	Cost/Hour	Typical Training Job	Outage at 75% Complete	Lost Value
1x A100	$1.29	48 hours	36 hours lost	$46.44
8x A100	$10.32	72 hours	54 hours lost	$557.28
1x H100	$2.49	96 hours	72 hours lost	$179.28

Mitigation:

# Save checkpoints frequently during training
import torch

def train_with_checkpoint_recovery(model, dataloader, checkpoint_dir):
    checkpoint_path = f"{checkpoint_dir}/checkpoint_latest.pt"
    start_epoch = 0
    
    # Try to recover from checkpoint
    if os.path.exists(checkpoint_path):
        checkpoint = torch.load(checkpoint_path)
        model.load_state_dict(checkpoint['model_state'])
        start_epoch = checkpoint['epoch'] + 1
        print(f"Recovered from epoch {start_epoch}")
    
    for epoch in range(start_epoch, total_epochs):
        # Training loop
        for batch in dataloader:
            # ... training code ...
        
        # Save checkpoint every epoch
        torch.save({
            'epoch': epoch,
            'model_state': model.state_dict(),
            'optimizer_state': optimizer.state_dict(),
            'loss': current_loss
        }, checkpoint_path)
        
        # Also save periodic full checkpoints
        if epoch % 10 == 0:
            torch.save({...}, f"{checkpoint_dir}/checkpoint_epoch_{epoch}.pt")

Research Deadlines and Paper Submissions

Academic context: Conference deadlines (NeurIPS, ICML, CVPR) are non-negotiable. Missing submission by even minutes means waiting another year.

Critical period risk:

• Final experiments running 48 hours before deadline
• Lambda Labs outage in last 24 hours
• No time to spin up alternatives (Modal, RunPod)
• Paper submission impossible without results

Risk mitigation:

• Start experiments 5-7 days before deadline, not 2-3 days
• Maintain accounts on 2-3 GPU providers
• Pre-test instance launch on backup providers
• Keep emergency budget for premium on-demand pricing

Production Inference Outages

For AI applications serving real users:

Lambda Labs isn't primarily designed for production inference (better suited for training), but many startups use it for cost-effective inference during early stages.

Outage impact:

• Customer-facing AI features offline
• User-generated content processing halted
• Real-time AI endpoints timing out
• SLA breaches with customers

Example: AI image generation service:

• 10,000 requests/hour at peak
• Lambda Labs instance down for 3 hours
• 30,000 failed requests
• Customer refunds + churn

Better architecture:

# Multi-provider inference routing
import random

PROVIDERS = [
    {"name": "lambda", "endpoint": "http://lambda-instance:8000"},
    {"name": "runpod", "endpoint": "http://runpod-instance:8000"},
    {"name": "modal", "endpoint": "https://modal-app.modal.run"}
]

def inference_with_fallback(input_data):
    providers = PROVIDERS.copy()
    random.shuffle(providers)  # Load balance
    
    for provider in providers:
        try:
            response = requests.post(
                f"{provider['endpoint']}/predict",
                json=input_data,
                timeout=30
            )
            if response.ok:
                return response.json()
        except Exception as e:
            print(f"{provider['name']} failed: {e}")
            continue
    
    raise Exception("All inference providers down")

GPU-Hour Budget Waste

The silent cost: Instances left running during outages still accrue charges.

Scenario:

• You launch 4x A100 instances ($5.16/hour total)
• Start training, step away
• Lambda Labs has storage infrastructure issues
• Training fails immediately but instances keep running
• You notice 8 hours later
• Wasted cost: $41.28 + zero progress

Prevention:

#!/bin/bash
# training_watchdog.sh - Monitor training progress, terminate if stalled

INSTANCE_ID="YOUR_INSTANCE_ID"
API_KEY="YOUR_API_KEY"
LOG_FILE="/home/ubuntu/training.log"
CHECK_INTERVAL=300  # 5 minutes

last_size=$(ssh ubuntu@INSTANCE_IP "stat -f%z $LOG_FILE")

while true; do
    sleep $CHECK_INTERVAL
    current_size=$(ssh ubuntu@INSTANCE_IP "stat -f%z $LOG_FILE" 2>/dev/null)
    
    if [ "$current_size" == "$last_size" ]; then
        echo "Training log not growing - terminating instance"
        curl -X POST "https://cloud.lambdalabs.com/api/v1/instances/$INSTANCE_ID/terminate" \
          -H "Authorization: Bearer $API_KEY"
        break
    fi
    
    last_size=$current_size
done

What to Do When Lambda Labs Goes Down

1. Verify It's Actually Down (Not Capacity)

Quick checklist:

# 1. Check API health
curl -I https://cloud.lambdalabs.com/api/v1/instance-types

# 2. Check status page
curl -s https://status.lambdalabs.com | grep -i "operational"

# 3. Check automated monitoring
# Visit https://apistatuscheck.com/api/lambda-labs

# 4. Check community
# Twitter/X: Search "lambda labs down"
# Discord: Lambda Labs community server

If API returns errors AND status page shows incidents AND apistatuscheck.com confirms issues, it's a real outage.

2. Secure Your Training Progress

If you still have SSH access:

# Quick backup of critical model checkpoints
INSTANCE_IP="your-instance-ip"
BACKUP_DIR="./emergency_backup_$(date +%Y%m%d_%H%M%S)"

mkdir -p "$BACKUP_DIR"

# Copy latest checkpoints
scp -r ubuntu@$INSTANCE_IP:/home/ubuntu/checkpoints/*.pt "$BACKUP_DIR/"

# Copy training logs
scp ubuntu@$INSTANCE_IP:/home/ubuntu/training.log "$BACKUP_DIR/"

# Copy wandb logs if using
scp -r ubuntu@$INSTANCE_IP:/home/ubuntu/wandb "$BACKUP_DIR/"

echo "Backup saved to $BACKUP_DIR"

If SSH is down but instance still running:

• Hope your training code is checkpointing to external storage (S3, Weights & Biases, HuggingFace Hub)
• Monitor recovery via logs if they're streaming to external service

3. Spin Up Backup Instances on Alternative Platforms

Have these accounts ready:

RunPod - Similar GPU cloud, often better availability:

# RunPod CLI for quick launch
runpod launch --gpu-type "A100" --image "pytorch" --ssh-key-file ~/.ssh/id_rsa.pub

Modal - Serverless GPU, great for inference and training:

import modal

stub = modal.Stub("emergency-training")

@stub.function(gpu="A100", timeout=86400)
def train_model():
    import torch
    # Your training code here
    pass

if __name__ == "__main__":
    with stub.run():
        train_model.remote()

Google Colab Pro+ - Quick fallback for smaller experiments:

• A100 access (limited time)
• Easy notebook interface
• Not suitable for multi-day training

Comparison matrix:

Provider	A100 Price/hr	Availability	Setup Time	Best For
Lambda Labs	$1.29	Medium	2 min	Long training
RunPod	$1.39	Good	3 min	Training + inference
Modal	~$1.10	Excellent	5 min	Serverless workloads
Colab Pro+	$50/mo	Good	Instant	Notebooks/prototyping

4. Implement Automatic Failover for Production

Multi-cloud orchestration with Kubernetes:

# k8s-gpu-deployment.yaml
apiVersion: v1
kind: Service
metadata:
  name: ml-inference
spec:
  selector:
    app: ml-model
  ports:
    - protocol: TCP
      port: 80
      targetPort: 8000
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ml-inference-runpod
spec:
  replicas: 1
  selector:
    matchLabels:
      app: ml-model
      provider: runpod
  template:
    metadata:
      labels:
        app: ml-model
        provider: runpod
    spec:
      nodeSelector:
        cloud.provider: runpod
      containers:
      - name: model
        image: your-model:latest
        resources:
          limits:
            nvidia.com/gpu: 1

This setup maintains instances on both providers, with automatic traffic routing away from failed endpoints.

5. Set Up Comprehensive Monitoring

Multi-layer monitoring stack:

# lambda_monitor.py - Comprehensive health checker
import requests
import time
from datetime import datetime

def check_api_health(api_key):
    """Check Lambda Labs API"""
    try:
        response = requests.get(
            "https://cloud.lambdalabs.com/api/v1/instance-types",
            headers={"Authorization": f"Bearer {api_key}"},
            timeout=10
        )
        return response.status_code == 200
    except:
        return False

def check_instance_ssh(instance_ip):
    """Check SSH connectivity"""
    import subprocess
    result = subprocess.run(
        ["ssh", "-o", "ConnectTimeout=5", f"ubuntu@{instance_ip}", "echo ok"],
        capture_output=True,
        timeout=10
    )
    return result.returncode == 0

def check_gpu_visibility(instance_ip):
    """Check if GPUs are visible on instance"""
    import subprocess
    result = subprocess.run(
        ["ssh", f"ubuntu@{instance_ip}", "nvidia-smi"],
        capture_output=True,
        timeout=10
    )
    return "NVIDIA-SMI" in result.stdout.decode()

def send_alert(message):
    """Send alert via webhook"""
    requests.post(
        "https://hooks.slack.com/services/YOUR/WEBHOOK/URL",
        json={"text": f"🚨 Lambda Labs Alert: {message}"}
    )

# Monitor loop
while True:
    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    
    if not check_api_health(API_KEY):
        send_alert(f"[{timestamp}] API health check failed!")
    
    if INSTANCE_IP and not check_instance_ssh(INSTANCE_IP):
        send_alert(f"[{timestamp}] SSH to {INSTANCE_IP} failed!")
    
    if INSTANCE_IP and not check_gpu_visibility(INSTANCE_IP):
        send_alert(f"[{timestamp}] GPUs not visible on {INSTANCE_IP}!")
    
    time.sleep(60)

Subscribe to external monitoring:

• API Status Check alerts - automated 24/7 monitoring
• Lambda Labs status page notifications
• Community Discord channels
• Twitter alerts for @LambdaAPI mentions

6. Post-Outage Recovery Checklist

Once Lambda Labs service is restored:

Immediate actions:

1. Verify API access - Test instance launch before committing
2. Check running instances - Ensure they resumed properly
3. Verify storage mounts - Confirm /home and persistent storage accessible
4. Test GPU visibility - Run nvidia-smi on all instances
5. Resume training jobs - Start from last checkpoint

Data reconciliation:

# Check for filesystem corruption
ssh ubuntu@INSTANCE_IP "sudo fsck -n /dev/sda1"

# Verify checkpoint integrity
python -c "
import torch
checkpoint = torch.load('checkpoint.pt')
print('Checkpoint valid:', checkpoint['epoch'])
"

# Resume training
nohup python train.py --resume-from checkpoint.pt &

Financial audit:

• Review billing for instances that ran without making progress
• Submit support tickets for credits if appropriate
• Document downtime for expense reports

Update documentation:

• Record incident details in team wiki
• Update runbooks with lessons learned
• Improve monitoring based on gaps discovered

Frequently Asked Questions

How often does Lambda Labs go down?

Lambda Labs maintains good overall uptime (typically 99.5%+), but the distributed nature of GPU infrastructure means issues can affect specific regions or GPU types without impacting the entire platform. Major outages affecting all services are rare (2-4 times per year), but partial issues—such as specific regions being unavailable or storage problems—occur more frequently. GPU capacity constraints (sold-out A100s/H100s) are common but distinct from true outages.

What's the difference between "No GPUs available" and Lambda Labs being down?

"No GPUs available" is Lambda Labs functioning normally but at capacity—they've allocated all physical GPUs. The API returns a successful response (HTTP 200) with a message about no capacity. Lambda Labs down means the API returns errors (500/502/503), times out completely, or the dashboard is inaccessible. During high-demand periods (like major AI conferences or model releases), capacity constraints are normal and not a service issue.

Does Lambda Labs offer SLA guarantees?

Lambda Labs does not currently offer formal SLA guarantees or uptime credits for their cloud GPU service. Unlike AWS or GCP with 99.99% SLAs and financial credits for breaches, Lambda Labs operates more like a cost-optimized GPU provider where lower prices come with less formal guarantees. For mission-critical production workloads requiring SLAs, consider enterprise providers or implement multi-cloud redundancy across Lambda Labs, RunPod, and Modal.

Can I recover my training data if Lambda Labs instance is terminated?

Lambda Labs instances have both ephemeral storage (lost on termination) and optional persistent storage. By default, data in /home is ephemeral—if Lambda Labs terminates your instance or it crashes, unsynced data is lost. Best practices:

• Save checkpoints to external storage (S3, GCS, HuggingFace Hub)
• Use rsync to continuously sync critical data off-instance
• Enable persistent storage volumes when launching instances
• Use experiment tracking tools (Weights & Biases, Neptune.ai) that automatically sync

Should I use Lambda Labs for production inference?

Lambda Labs is primarily designed for training, not production inference. While it works for inference and is cost-effective during early stages, consider these limitations:

• No auto-scaling or serverless options
• Manual instance management required
• No built-in load balancing
• Restarts require manual intervention
• Less reliable than managed inference platforms

For production inference, consider: Modal (serverless GPU), Replicate (managed inference), AWS SageMaker, or keep Lambda Labs as a cost-effective backup tier behind primary providers.

How do I get notified immediately when Lambda Labs goes down?

Set up multi-channel alerting:

1. Subscribe to API Status Check - real-time monitoring with instant alerts via email, Slack, Discord, or webhook
2. Enable Lambda Labs status page notifications at status.lambdalabs.com
3. Implement your own health checks - run the monitoring script from section "What to Do When Lambda Labs Goes Down"
4. Join community channels - Lambda Labs Discord, ML Twitter/X communities often report issues before official channels
5. Monitor instance-level health - set up SSH watchdogs and GPU visibility checks

What's the fastest alternative to Lambda Labs when it's down?

For immediate GPU access:

• Modal - Deploy code in ~5 minutes, serverless GPUs, excellent availability
• RunPod - Similar to Lambda Labs, often has better A100 availability, ~3 minute setup
• Google Colab Pro+ - Instant access if you just need to continue working on notebooks

Setup time comparison:

• Modal: 5 min (install CLI, deploy function)
• RunPod: 3 min (launch pod, SSH setup)
• Colab Pro+: Instant (open browser, attach GPU)
• AWS EC2 P4d: 10-15 min (account setup, instance config, AMI)

Pre-create accounts on 2-3 alternatives so you can switch instantly during outages. Many ML engineers maintain "hot standby" accounts with SSH keys and environment setups ready.

Can I get a refund for lost compute time during Lambda Labs outages?

Lambda Labs' terms of service do not guarantee refunds for downtime or lost compute time. However, they've been responsive to support requests in cases of significant service disruptions. Best approach:

1. Document the outage (screenshots, timestamps, API errors)
2. Calculate the impact (hours of instance time without progress)
3. Submit a detailed support ticket explaining the situation
4. Request account credits as goodwill

They've granted credits in past major incidents, but it's discretionary. Enterprise customers may have custom agreements with more formal recourse.

How do Lambda Labs outages compare to AWS/GCP/Azure GPU availability?

Lambda Labs:

• Lower cost (~30-50% less than hyperscalers)
• Higher outage frequency (minutes to hours, 2-4x/year)
• Capacity constraints more common
• No SLA guarantees

AWS/GCP/Azure:

• Higher cost but formal SLAs (99.99% with credits)
• Lower outage frequency but longer recovery when they occur
• Better redundancy and failover
• GPU capacity still constrained during shortages

Best of both worlds: Use Lambda Labs for development and training, AWS/GCP for production inference with SLA requirements. Many teams maintain Lambda Labs for 80% of work (cost savings) with cloud provider backup for critical deadlines.

Stay Ahead of Lambda Labs Outages

Don't let GPU infrastructure issues derail your ML projects. Subscribe to real-time Lambda Labs alerts and get notified instantly when issues are detected—before your training job fails.

API Status Check monitors Lambda Labs 24/7 with:

• 60-second health checks of API and GPU availability
• Instant alerts via email, Slack, Discord, or webhook
• Historical uptime tracking and incident reports
• Multi-provider monitoring for your entire ML infrastructure stack

Start monitoring Lambda Labs now →

Also monitor other GPU providers:

• RunPod Status - Alternative GPU cloud
• Modal Status - Serverless GPU platform
• HuggingFace Status - Model hosting and inference
• Weights & Biases Status - Experiment tracking

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Last updated: February 5, 2026. Lambda Labs status information is provided in real-time based on active monitoring. For official incident reports, always refer to status.lambdalabs.com.