Q: GPU capacity shortages often prevent procuring large clusters on AWS. How do you architect a multi-cloud compute strategy that trains models on specialized neo-clouds (CoreWeave/Lambda Labs/Nebius) while retaining your core data lakes, CI/CD, and model registries in AWS?

Interconnecting AWS with CoreWeave requires robust networking without multi-gigabit public internet exposure. Deploy dedicated Megaport / Equinix Cloud Exchange direct cross-connects, or establish an encrypted WireGuard mesh using Tailscale or Netmaker. Ensure MTU is optimized (9000 jumbo frames where supported) to avoid packet fragmentation.

# WireGuard point-to-point cross-cloud tunnel interface
[Interface]
PrivateKey = <private_key>
Address = 10.200.0.1/24
ListenPort = 51820

[Peer]
PublicKey = <peer_key>
Endpoint = coreweave-gw.customer.internal:51820
AllowedIPs = 10.200.0.0/24

Never stream training batches live across clouds over the internet during distributed training. Instead, stage training datasets asynchronously: mirror WebDataset shards or parquet chunks from Amazon S3 to the specialized cloud's high-speed local storage (Ceph / Weka / VAST Data) prior to job launch using rclone or AWS DataSync.

# Multi-threaded parallel dataset staging before training launch
rclone copy --transfers=64 --checkers=32 s3:training-datasets /mnt/vast/datasets

Run the training control plane using a unified batch orchestrator (Flyte, Kubeflow, or Slurm federated via git). When the training run finishes on CoreWeave, worker jobs write the final model weights to a local buffer and trigger an automated background sync back to the enterprise AWS S3 model registry, where downstream CI/CD deployment pipelines take over.

# Post-training sync hook
aws s3 cp /mnt/vast/checkpoints/final-step s3://prod-model-registry/model-v2/ --recursive