The probe finished. Median is 79.36 s/it across ten timed steps with a
min/max of 79.30 to 79.45, and peak memory is 75.1 of 121.6 GiB by
PyTorch's own max_memory_allocated -- 46 GiB spare rather than the 35 I
estimated from a live free reading, which was counting fragmentation and
the resident model rather than the allocation high-water mark.
Resolved attention backend recorded as flex_attention, read off the loaded
model rather than trusted from the request, which is the check I got wrong
the first time.
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