Challenges of scheduling local AI compute across 5090, Mac Studio, and eGPUs
ShittyMillennial · reddit · 2026-08-03
A developer shared their complex local AI hardware stack, consisting of an RTX 5090 PC, a Dell server, an M3 Ultra Mac Studio with 256GB unified memory, and a Thunderbolt-connected 5090 eGPU.
The core pain points revolve around heterogeneous hardware and network bottlenecks:
- Compute Silos: The 5090 eGPU is hard to fully utilize by the Mac (lacking optimized Nvidia inference engines), while the older Dell server is bottlenecked by PCIe 3.0.
- Cross-device inference limits: Although all devices are on a 10GbE network, executing tensor parallelism or splitting layers across the network is highly inefficient.
The user is seeking advice to optimize their workflow, primarily focused on agentic coding and automation, hoping to leverage the Mac Studio's massive memory for larger models to reduce reliance on cloud orchestration.
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