GPUs have become the most expensive resource in modern computing, and yet most organizations operate them with little visibility. A single training cluster can run for days and cost millions of dollars.
Most organizations pool these accelerators across teams on shared clusters to keep them busy. But in these shared environments, platform teams cannot answer basic questions: which team is using which accelerators?
Is the inference workload generating tokens efficiently? When a workload degrades (a failing accelerator, a thermally throttled node), teams find out only after a run crashes or the bill arrives. The device-level signals needed to find the cause (request traces, kernel logs, memory metrics from the accelerator itself) are not collected or connected.
