Multi-Cloud LLM Training Architecture: GCP, AWS & RunPod GPU Infrastructure

Introduction

Training large language models (LLMs) requires substantial GPU compute resources that traditional cloud providers may not always offer at competitive prices. RunPod has emerged as a popular alternative, providing access to high-end GPUs like A100 and H100 at significantly lower costs than AWS or GCP.

However, many enterprises already have their training data stored in cloud platforms like AWS S3 or Google Cloud Storage. This creates a common architectural challenge: how do you efficiently and securely transfer data from your existing cloud infrastructure to external GPU compute providers like RunPod?

This article provides a complete reference architecture for multi-cloud LLM training, covering:

1. RunPod Networking Options - Cloud Sync, Global Networking, and optional WireGuard
2. Cloud Data Storage - GCP and AWS data lake patterns
3. Docker Image Management - Where to store and pull training container images
4. Network Architecture - CIDR planning and secure connectivity
5. LLM Fine-Tuning Data Flow - End-to-end training pipeline

Key Finding from Research: RunPod does NOT support native VPC peering with external clouds. Data transfer happens via Cloud Sync (TLS/HTTPS) or optional self-hosted WireGuard tunnels.

Multi-Cloud Training Architecture Overview

Architecture Components

RunPod Networking: What You Need to Know

RunPod offers several networking features, but it's crucial to understand their capabilities and limitations.

Global Networking (Internal)

RunPod's Global Networking creates a secure, private network connecting all your Pods within your account:

Connectivity Options Comparison

GCP Network Architecture

When your training data lives in GCP, implement a hub-spoke VPC architecture for optimal security and scalability.

GCP CIDR Planning

GCP Best Practices

1. Private Service Connect - Access GCS without public IPs
2. VPC Service Controls - Prevent data exfiltration
3. Cloud NAT - Controlled egress for RunPod API calls
4. Cloud VPN - Optional WireGuard gateway

AWS Network Architecture

For AWS-based training data, use a multi-subnet VPC with VPC endpoints for private access.

AWS CIDR Planning

AWS Best Practices

1. S3 Gateway Endpoint - Free private S3 access
2. ECR Interface Endpoint - Private Docker pulls
3. Security Groups - Least privilege access
4. VPN Endpoint - Optional WireGuard connectivity

Docker Image Storage

Registry Comparison

Docker Image Strategy

# CI/CD Pipeline for Multi-Registry Push
name: Build and Push
on:
  push:
    branches: [main]
    paths: ['docker/**']

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Build Image
        run: docker build -t llm-trainer:${{ github.sha }} .

      # Push to GCP Artifact Registry
      - name: Push to GCR
        run: |
          gcloud auth configure-docker us-central1-docker.pkg.dev
          docker tag llm-trainer:${{ github.sha }} \
            us-central1-docker.pkg.dev/project/repo/llm-trainer:${{ github.sha }}
          docker push us-central1-docker.pkg.dev/project/repo/llm-trainer:${{ github.sha }}

      # Push to AWS ECR
      - name: Push to ECR
        run: |
          aws ecr get-login-password | docker login --username AWS --password-stdin \
            ${{ secrets.AWS_ACCOUNT }}.dkr.ecr.us-east-1.amazonaws.com
          docker tag llm-trainer:${{ github.sha }} \
            ${{ secrets.AWS_ACCOUNT }}.dkr.ecr.us-east-1.amazonaws.com/llm-trainer:${{ github.sha }}
          docker push ${{ secrets.AWS_ACCOUNT }}.dkr.ecr.us-east-1.amazonaws.com/llm-trainer:${{ github.sha }}

LLM Fine-Tuning Data Flow

Training Pipeline Stages

Cloud Sync Configuration

# RunPod Cloud Sync for LLM Training
import runpod
from runpod.cloud_sync import CloudSync

# Initialize sync with GCS
gcs_sync = CloudSync(
    provider="gcs",
    credentials_json=os.environ["GCS_SERVICE_ACCOUNT"],
    bucket="training-data-bucket",
    local_path="/workspace/data"
)

# Sync training data
gcs_sync.download(
    source_path="datasets/llm-finetune/",
    include_patterns=["*.parquet", "*.jsonl"]
)

# After training, sync checkpoints back
s3_sync = CloudSync(
    provider="aws",
    access_key=os.environ["AWS_ACCESS_KEY"],
    secret_key=os.environ["AWS_SECRET_KEY"],
    region="us-east-1",
    bucket="model-checkpoints-bucket",
    local_path="/workspace/checkpoints"
)

s3_sync.upload(
    destination_path=f"run-{run_id}/",
    include_patterns=["*.pt", "*.safetensors"]
)

CIDR Planning for Multi-Cloud

Complete CIDR Allocation

Secure Network Path

Security Layers

When to Use WireGuard vs Cloud Sync

Based on our research, here's the decision framework:

Use Cloud Sync (Default) When:

• Standard LLM training workflows
• Data is not extremely sensitive
• You want simple, managed connectivity
• You're okay with 100 Mbps throughput

Use WireGuard When:

• Regulatory requirements prohibit public endpoints
• You need private IP connectivity
• You want to avoid exposing any ports publicly
• You're running sensitive workloads

WireGuard is FREE and Open Source

WireGuard is licensed under:

• Kernel components: GPLv2
• User-space tools: GPL-2.0, MIT, BSD, Apache 2.0

You can deploy WireGuard at no licensing cost—you only pay for the VM/bandwidth.

Best Practices Summary

1. Data Storage

• Use private endpoints for GCS/S3 access
• Implement VPC Service Controls (GCP) or VPC endpoints (AWS)
• Enable encryption at rest with customer-managed keys

2. Networking

• Single cloud? Use native VPC networking
• Multi-cloud? Consider AWS-GCP Interconnect (new in 2025)
• External GPU? Use Cloud Sync or WireGuard

3. Docker Images

• Push to both Artifact Registry and ECR
• Use VPC endpoints for private pulls
• Implement image signing and vulnerability scanning

4. Security

• Never hard-code API keys in scripts
• Use environment variables or secrets managers
• Implement least privilege IAM policies
• Enable audit logging for all data access

Conclusion

Building a multi-cloud LLM training architecture requires understanding the networking capabilities and limitations of each platform. Key takeaways:

1. RunPod uses Cloud Sync for external data transfer—there's no native VPC peering
2. Global Networking provides 100 Mbps Pod-to-Pod connectivity within RunPod
3. WireGuard is optional for cases requiring private connectivity without public ports
4. Docker images should be stored in cloud registries (Artifact Registry, ECR) with VPC endpoints
5. CIDR planning must avoid overlaps between GCP, AWS, and tunnel networks

Further Reading

• RunPod Global Networking Documentation
• RunPod Cloud Sync Guide
• WireGuard Official Site
• AWS Interconnect - Multicloud
• GCP Cross-Cloud Interconnect