Running Turnstone Across Local AI Workstations

I demonstrate Turnstone, a tool for coordinating AI agents across local machines, and use it to delegate software-development tasks to different models and systems. Along the way, I compare the NVIDIA DGX Station with a system powered by eight RTX PRO 6000 GPUs, including examples of workloads, throughput, power use, and task completion times.

■ Turnstone setup and use
- Configure models, personas, nodes, and shared workspaces; delegate repository changes and issue reviews to multiple agents.
- Follow examples involving Windows on ARM compatibility, code testing, auditing, and video generation.

■ Hardware and practical considerations
- Compare memory architectures and workloads across the DGX Station, RTX PRO 6000 system, and DGX Spark nodes.
- Review remote management, power consumption, multi-user throughput, and estimated cloud API costs; the comparison is based on specific demonstrations, not a standardized review of every workload.

For developers and teams considering local AI infrastructure, these examples show how to assess model placement and agent workflows. Use them as a starting point to test your own tasks, hardware, and operating costs before choosing a setup.