University of Michigan - Ann Arbor

To adapt to shifting VRAM availability and growing Key-Value (KV) cache demands during execution, the serving engine dynamically reconfigures its GPU memory allocation without process restarts. After dedicating VRAM to non-expert weights and fixed runtime structures, the system partitions the remaining memory between KV cache pages and complete-expert cache slots. At any scheduler safe point, the runtime can rebuild the GPU expert cache to match a revised VRAM budget and re-establish its static execution graph. Because the CPU-resident expert pool acts as the permanent source of truth for all expert weights, altering or flushing the GPU expert cache affects only inference performance rather than computation correctness.

Runtime Cache Reconfiguration at Scheduler Safe Points

Match each memory or execution concept to its operational role during runtime reconfiguration.

Order the stages involved in memory allocation and runtime cache reconfiguration during execution.

Explain why flushing and reducing complete-expert cache slots on the GPU increases latency while leaving computation correctness completely unaffected.

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