Phoenix Lee

This article unpacks how it uses independent environment layers and on-demand reads to cut preparation overhead, memory sharing and reclamation with CPU scheduling to carry large numbers of concurrent sessions, and decoupling from preemptible GPU training to preserve execution progress. Public experiments verify the gains of some mechanisms, but they are not yet enough to fix the ratio between environment and GPUs. Judging such platforms also depends on the supply of valid training trajectories, recovery overhead, and total cost.

This piece reads each in turn — how real the “3×” is against Ascend 960 and NVIDIA B300, where RSI’s boundaries sit, and why the unit of competition has moved from the single chip to the whole system.

The piece draws the commercial status line first (buyable, deploying, or calendar), then unpacks the two compute legs, the four cluster numbers and one verification chain, how the M900 writes storage into the execution path, and delivery as combinations across cloud and industry. The conclusion: a system, not a catalog, and the unit of competition is shifting from single products to whole systems.