Team weighs multi-GPU cluster vs one fat workstation to escape cloud API rate limits
Decent_Wing_4393 · reddit · 2026-11-07
A team hitting API rate limits from parallel agent workflows is weighing bringing inference in-house, and lays out the tradeoffs in detail.
Key dilemmas:
- One fat workstation: huge context windows, but prefill pauses hurt in agent loops that re-read files constantly
- Multiple smaller nodes: fast prefill, but 200GB of total VRAM doesn't merge into one pool — strict physical placement constraints
- GPU sharing is a nightmare: scheduling who gets what, killing orphaned processes squatting on VRAM, manual SSH burns people out
They note vLLM already handles serving and distributed inference; what's missing is a management layer for deployments, GPU placement and team access — GPUStack-style platforms fill that gap. They ask the community whether a scheduler layer actually stops resource fights or just adds failure modes, and whether owned hardware truly replaced cloud APIs.
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