Cloud GPU Infrastructure: Navigating AWS H100s, RunPod, and Serverless GPUs

Renting cloud GPUs for LLM fine-tuning and inference can get expensive quickly if you choose the wrong provider or instance type. Between AWS EC2, specialized GPU clouds like RunPod/Lambda Labs, and serverless compute like Modal, here is a practical breakdown of GPU economics and deployment strategies.

Hyperscalers (AWS / GCP / Azure) vs Specialized Cloud Providers

• Hyperscalers (AWS EC2 p5.48xlarge, g5.2xlarge): High availability, enterprise SOC2 compliance, and tight integration with S3/VPC. However, pricing is steep ($3.80/hr+ for A10G, $30+/hr for H100s).
• Specialized GPU Clouds (RunPod, Lambda, DeepInfra): Up to 60% cheaper hourly rates for H100/A100 instances. Ideal for batch fine-tuning runs and non-critical batch processing.

Serverless GPU Workloads (Modal / RunPod Serverless)

For intermittent workloads (e.g., generating embeddings or batch document OCR), serverless GPU functions scale to zero when idle:

# Example Modal Serverless GPU Deployment
import modal

app = modal.App("llm-inference-service")
image = modal.Image.debian_slim().pip_install("vllm")

@app.function(gpu="A10G", timeout=300)
def generate_text(prompt: str):
    from vllm import LLM
    llm = LLM(model="meta-llama/Meta-Llama-3-8B-Instruct")
    return llm.generate([prompt])

Key Takeaways

1. Cold Starts: Serverless GPUs experience 10-30 second cold starts due to container image pulling and model weight loading into VRAM. Use warm instances for user-facing interactive chat APIs.
2. Spot Instances: Use spot/interruptible instances for long training jobs with automatic checkpointing to S3/R2 every 100 steps.