Lizard inference engineering: Missing is not zero
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Inference Engineering · Day 9 · Evening
A benchmark dashboard can mislead without containing a false number.
Treat an excluded provider as zero. Mix warm-up into decode. Turn one metric winner into a universal recommendation. Each shortcut changes the story.
Lizard keeps provider identity, wall time, end-to-end throughput, native decode, load, warm-up and memory separate. Missing providers stay unmeasured.
The goal is not to remove judgment. It is to keep the evidence needed for judgment.
Engineering fact: Lizard's dashboard keeps excluded providers unmeasured and separates wall time, end-to-end throughput, decode, warm-up, and memory.
#Benchmarking #DataIntegrity #LLMInference #Reproducibility #LocalAI
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