Foundation Model Foundry Ramp Plan

Large clusters are now shared across six research squads plus two product inference groups. We outline a four-phase migration to pooled scheduling that keeps the launch cadence intact while also unlocking 20% higher GPU utilization.

Control planes

A shared scheduler running atop Ray Serve gives researchers self-service access to 512-GPU shards with spot/flex mixes defined via policy. Product teams continue to pin critical inference services to on-demand nodes, but we carve out an emergency burst pool to satisfy surprise demo requirements.

Funding model

Each division pre-pays for baseline capacity while the central AI office meters incremental boosts weekly. Finance receives a transparent ledger of GPU hours with tags by objective and owner, so they can forecast demand a quarter out.