Design a multi-tenant GPU serving system for hundreds of fine-tuned models
Tests GPU memory tradeoffs versus cold-start latency in multi-tenant serving. Strong answers propose tiered CPU staging, predictive pre-warming, and disaggregated prefill and decode. Red flag: keeping all models GPU-resident or ignoring transfer overhead.
Tests architecting a multi-tenant inference system that dynamically loads hundreds of fine-tuned models without exhausting GPU memory while bounding cold-start latency. Strong answers cover tiered memory hierarchies using CPU RAM and fast NVMe staging, predictive pre-warming from usage patterns, disaggregated prefill and decode across nodes, SLO-aware scheduling with queueing during load, and extending GPU memory via storage caching. Red flag: assuming all weights stay GPU-resident or neglecting PCIe and NVLink transfer latency.
Read the original → developer.nvidia.com
- #mlops
- #gpu inference
- #model serving
- #distributed systems
- #memory management
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