I'm building a hybrid setup with cloud and owned GPUs

Nice — a hybrid cloud + owned-GPU setup can work really well.

What are you trying to optimize for?

• Cost: keep steady workloads on owned GPUs, burst to cloud when needed
• Latency: run inference closer to users, keep training centralized
• Scalability: own baseline capacity, rent extra for spikes
• Privacy / compliance: keep sensitive data on-prem, use cloud for less sensitive jobs
• Reliability: cloud as failover, or owned GPUs as the main pool

A few common hybrid patterns:

1. Owned GPUs for baseline, cloud for burst

   • Best if your workload is spiky.
   • Example: inference service runs on your hardware; batch jobs and retries spill over to cloud.

2. Cloud for training, owned GPUs for inference

   • Good when training is intermittent and heavy.
   • Keeps always-on serving costs lower on owned hardware.

3. Owned GPUs for sensitive data, cloud for public/auxiliary jobs

   • Useful for regulated environments.
   • You can split pipelines by data classification.

4. Unified scheduler across both

   • Use a job queue or orchestration layer to route tasks based on availability, cost, and SLA.
   • Common with Kubernetes, Ray, Slurm, or custom dispatchers.

Key design considerations:

• Network bandwidth and egress costs
• GPU memory compatibility and model sharding
• Autoscaling rules
• Checkpointing and job preemption
• Observability across both environments
• Secret management and access control

If you want, I can help you design:

• a reference architecture
• a cost model
• a scheduler/routing strategy
• or a stack recommendation based on your workload

If you share your use case, I can make it concrete.