GPU Cloud Providers Comparison

Overview

Choosing the right GPU cloud can save thousands of dollars. This guide compares pricing, availability, and features across providers.

Provider Comparison

Vast.ai

Best for: Budget training, spot instances

# Install CLI
pip install vastai

# Search for GPUs
vastai search offers "gpu_name=RTX_4090 num_gpus=1"

# Create instance
vastai create instance <offer_id> --image pytorch/pytorch:latest

Pros: Cheapest prices, community GPUs Cons: Variable reliability, no SLA

RunPod

Best for: Serverless inference, quick experiments

# Serverless endpoint
runpodctl deploy --gpu-type "NVIDIA A100" --template "runpod/pytorch"

Pros: Easy serverless, good UI Cons: Slightly higher prices than Vast.ai

Lambda Labs

Best for: Reliable training, reserved capacity

Pros: Consistent availability, good support Cons: Hourly billing minimum

When to Use Each

Cost Optimization Tips

1. Use spot/interruptible: 50-70% savings
2. Right-size GPUs: Don’t use H100 for inference
3. Preemptible training: Checkpoint frequently
4. Multi-GPU: Often cheaper than single larger GPU
5. Off-peak hours: Prices drop at night/weekends

Key Resources

• Vast.ai
• RunPod
• Lambda Labs