Instrument the live server to answer "can -ot target the most active
layers": AMD IBS op sampling on llama-server during 4 mixed concurrent
generations, sampled load addresses mapped through /proc/<pid>/maps to
backing tensors with extents derived from consecutive GGUF header
offsets (no quant type-size tables - a wrong per-element size silently
mis-buckets tensor tails, first run lost 59% of expert samples to a
junk bucket).
Measured (75 s window, 2.7M expert-tensor samples): per-block read
volume is FLAT, 0.93x-1.08x of mean across all 40 CPU-resident blocks.
MoE top-k routing fires every block every token, so "most active
layers" do not exist at -ot granularity - any 8 GPU-resident blocks
are equivalent, the lever is COUNT, already at the 5/3 VRAM ceiling.
Real skew is per-EXPERT (top 10% of 48x512 cells carry 40.5% of
samples), below tensor-override granularity. No config change.
scripts/frank-primary-expert-activity.py: dump + sample + analyze in
one entry point; --analyze-only re-analyzes saved samples. Instruments
production serving in place; not a benchmark.
Frank
f41e0384dd