inference

Separate valid 2:4 structure, packed representation and sparse dispatch from model quality and the fraction of request time that can improve.

Diagnose a stream that begins quickly but produces useful answer content late. Compare generation, parser and client clocks, then test a reasoning budget without hiding truncated or incorrect responses.

Compare tensor, pipeline and expert traffic with per-request KV transfer. Work through declared payload and latency assumptions before choosing a serving network.

Size usable inference headroom from the complete readiness delay and demand ramp, then decide which training capacity can safely be borrowed.

Make readiness mean that the engine can serve, scale from measured demand and ready capacity, and drain streams before termination. Kubernetes can implement these controls; an operator packages and reconciles them.

Find concrete failure paths in a small serving architecture. Rank correctness, admission and placement fixes before proposing additional infrastructure.