Question the measurement
My LLM judge was flipping a coin a third of the time
What happened when I tested the judge I had trusted for months.
Software engineer / Miami, FL
The notebook
Small thoughts, things I learned the hard way, and ideas that needed more room.
Follow via RSS — every new thought, field note, and essay in one feed.
Question the measurement
What happened when I tested the judge I had trusted for months.
Follow a fix
An exact failure, the hardware behind it, and the two-line fix.
Look at the craft
The engineering behind a quieter page transition on this site.
5 pieces about Inference & performance
The model card advertised a 4B multi-token-prediction head. No published GGUF contained it, and the architecture had no code path to run it. Both halves arrived within 36 hours from two different strangers, in incompatible forms — so I merged the head into the target file myself. It went from 27.75 to 43.30 tok/s, and three of my four mistakes along the way were about verification, not tensors.
A 125B model decoding at 26.6 tok/s looks unremarkable beside a 31B at 29.3 — until you notice the 31B is running a drafter, so its number is a forward-pass rate multiplied by an acceptance length. Per forward pass the big model was 3x faster. Here is how to decompose the number, and what the bandwidth arithmetic says is still on the table.
Four slots served my agentic pipeline at 1.2% prefix-cache reuse. One slot served the same pipeline at 86.7%, and saved 11.6 minutes of prefill in a 39-minute window. Slots schedule requests; they do not add compute — and on a single GPU they scatter the one thing a prefill-bound workload actually depends on.
I have swapped the local model under my newspaper 85 times in four months. That looked like a natural experiment, so I built an instrument to read it — including a gate that refuses comparisons contaminated by time. The gate passed. It was reading my switch log instead of my writing, and the two models it cleared had never once alternated.
Three times this year a reasoning model spent its whole token budget thinking and handed back empty content under HTTP 200. Three times, the code reading the result invented a different explanation — a parser bug, then an unconstrained grammar. Neither was true, and one of them fired inside a safety check.