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.
The notebook
Writing
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.
A few places to start
Follow a fix
Every FP8 GEMM traps on DGX Spark (SM121)
An exact failure, the hardware behind it, and the two-line fix.
Look at the craft
A page transition that breathes
The engineering behind a quieter page transition on this site.
Browse the notebook
5 pieces about Linux & hardware
Essay
The cache hit that answered the wrong question
I turned prefix caching back on for a 125B hybrid model on a DGX Spark, with the fixes carried from five open pull requests. Hits landed, a number planted deep in the prompt came back exactly every time, and an 11,000-token prompt dropped from 5.99 s to 0.96 s. Then one request answered the question two other requests were asking. Proving that was not a leak took a better instrument than the one I started with.
Essay
I grafted a speculative decoding head into a 90 GB model file
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.
Essay
The kernel kills your inference server first, and by default
An 87 GiB model got OOM-killed with nothing in its own log. The process that triggered it was a 27 MB dashboard service. The reason my inference server was the kernel first choice was not its size — it was a systemd user-manager default that scores every terminal-launched process to die before any system service. Then restarting it killed my editor.
Essay
Tokens per second told me the wrong model was faster
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.
Essay
I turned off concurrency and the server got faster
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.