SemiAnalysis profiles RL trainer-generator throughput

While the industry is obsessed with raw GPU counts, the true bottleneck in the frontier of AI reasoning models is system throughput matching and CPU-bound containerized environments.

* The transition from static pre-training to dynamic RL makes training loops highly asynchronous and bound by the speed of execution sandboxes.

* Policy staleness budgets introduce strict constraints, forcing trade-offs where developers must intentionally lower trainer Model Flops Utilization (MFU) to prevent starvation.

* Datacenter architecture must shift horizontally, scaling CPU and orchestration capabilities to manage sandbox latency rather than just scaling GPU clusters.