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Cloud-Native Alternative (High OpEx)

For users without access to a powerful local workstation, a cloud-based setup is possible, but it has significant limitations for robotics and Physical AI systems.

Cloud GPU Workstations​

Cloud providers such as AWS, Azure, and Google Cloud offer instances equipped with powerful NVIDIA GPUs.

  • AWS EC2 G5 instances — NVIDIA A10G GPUs
  • Azure NC-series — NVIDIA V100 or A100 GPUs
  • Google Cloud Compute Engine — NVIDIA T4, V100, or A100 GPUs

These instances provide on-demand access to GPU resources without the capital cost of purchasing hardware.

Isaac Sim via Omniverse Cloud​

NVIDIA Omniverse allows Isaac Sim to run on cloud GPUs while streaming the rendered output to a local machine.

How It Works​

  1. Isaac Sim runs on a cloud-hosted GPU instance
  2. Rendered frames are streamed to the local client
  3. Users interact through a browser or streaming client
  4. Control commands are sent back to the cloud instance

This enables simulation work from devices without dedicated GPUs.

Cost Considerations​

Cloud-based simulation introduces high operational expenditure (OpEx) driven by usage time.

Typical Cost Structure​

  • GPU instance rates — $0.50 to $5.00+ per hour (depending on GPU tier)
  • Storage costs — persistent volumes for simulation assets and models
  • Data transfer costs — inbound/outbound networking
  • Omniverse streaming fees — if using NVIDIA-managed services

Example:
Running 20 hours per week on a mid-tier GPU instance may cost $40–$100+ per week.

Critical Limitation​

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Local edge hardware (the Physical AI Edge Kit) is still required.

Real-time robotic control is not feasible over high-latency Internet connections.

Why Edge Hardware Is Non-Negotiable​

  • Latency — Cloud round-trip times (50–200 ms) are too slow for balance and manipulation tasks requiring sub-10 ms response
  • Reliability — Robots must function autonomously if connectivity drops
  • Safety — Control loops must execute locally to respond immediately to hazards

When Cloud Makes Sense​

Cloud-based simulation is appropriate in the following cases:

  • Students without access to RTX-class workstations
  • Infrequent or short-duration simulation workloads
  • Distributed teams sharing a common environment
  • Bursty workloads requiring temporary compute scaling

Hybrid Approach​

The most practical setup is often a hybrid model:

  1. Use a local workstation for daily development and debugging
  2. Use cloud resources for large-scale training or physics-heavy scenarios
  3. Always deploy inference and control to local edge hardware

This approach balances cost, performance, and safe real-world deployment.