During today’s 3 p.m. lab, my discourse-mapping drill morphed into a soap opera when a student used role shift for the narrator, the cat, and the toaster. I design that exercise to isolate perspective-taking and non-manual markers, but the laughter flagged gaps in turn-taking and referent tracking that my rubric missed. Do you ride the humor as formative data or reset with a tighter 60-second prompt to re-center the skill focus?
I ride the laughs as data, but I slap painter’s tape on the floor and assign lanes — center narrator, left cat, right toaster — so turn-taking’s visible and “the cat and the toaster” don’t hijack the scene. , when the narrator starts leaking the cat’s NMMs, I enforce a 2-second neutral reset and a quick head-tilt cue before each switch. If it’s still spaghetti in a 3 p.m. lab, I cap it at two referents per pass, then rotate — does that calm it for you?
, that about 210 W floor with 32% SM idle at “1–5 a.m.” is usually HBM/app clocks refusing to nap; I cut about 16% by scheduling a night profile that lowers HBM to 877 MHz via nvidia-smi -ac and toggles persistence mode off so P-states drop, paired with your 180 W cap. Most actionable Prometheus bits were dcgm_exporter’s power_usage, mem_clock, and clocks_throttle_reasons — watch the ‘app clock’ vs ‘sw power cap’ flags; GitHub - NVIDIA/dcgm-exporter: NVIDIA GPU metrics exporter for Prometheus leveraging DCGM. Caveat: if Triton models are memory‑bound, that HBM drop dings p95 — are those night requests mostly compute-heavy, @OP?