Introducing TPU Sync: Fast KV cache and weight transfer for inference and RL on TPU

As frontier AI evolves from stateless single-turn prompts to million-token agents, disaggregated serving, and continuous reinforcement learning (RL), the primary bottleneck in AI infrastructure has shifted from raw compute to orchestrating live model state — both the contextual state held in massive key-value (KV) caches during inference and the parameter state updated and broadcast from trainers to rollout samplers during RL. Whenever this state is trapped inside a single chip's memory or stal…

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