Lizard inference engineering: Same model name is not proof of same weights

[lizardadmin]

Inference Engineering · Day 12 · Morning

A model label tells you almost nothing about provenance.

Two runners can point at the same name and still resolve different GGUF files, different revisions, or different conversion paths. That is enough to change the meaning of any timing result. The useful check is not “did the label match?” but “did both runtimes resolve the same physical file?”

That is why Lizard’s HTTP comparison records the real GGUF path for both Lizard and llama.cpp, then treats the comparison as admissible only when both sides land on the same file on disk. It turns a benchmark from a trust exercise into a file-identity check.

In practice, that means you can separate engine behavior from model drift. If the path differs, the result is a comparison across two artifacts, not a clean engine-vs-engine result. If the path matches, you at least know the weights are the same before you argue about throughput, memory, or decode behavior.

I’ve found this matters most when model names get reused across quantizations and local copies. The filename looks stable, but the object behind it is not.

What would you require before you repeat a same-model benchmark claim in your own workflow?

Engineering fact: The HTTP comparison records the real GGUF path for both Lizard and llama.cpp and admits a performance claim only when both resolve to the same physical file.

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#llama_cpp #gguf #benchmarking #modelprovenance #localai

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