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Inference capacity becomes RL capacity

From Taking Reinforcement Learning Cross Datacenter — Nan Jiang, Modal · ≈18:18

The strategic consequence: scattered, autoscaled, multi-provider inference GPUs turn into one elastic rollout fleet no longer bounded by the trainer's fixed cluster size.

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The strategic consequence: scattered, autoscaled, multi-provider inference GPUs turn into one elastic rollout fleet no longer bounded by the trainer's fixed cluster size.

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first. By doing this we can have rollouts uh engines like autoscale globally. Each one self-s sync it weights serve accept version and return rollout metadata. That means scattered inference cap capacity became one elastic rollout fleet. Instead of being limited by the trainer cluster rollout can be the global pool. So inference capacity can now become our capacity.

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