A fast worker loop is not automatically a fast product.

If the benchmark only times an internal execution loop, it can hide the parts users feel immediately: request parsing, scheduling, serialization, and the wait for the full response. Lizard's native readiness benchmark avoids that trap by sending concurrent requests through the OpenAI-compatible HTTP endpoint and measuring full-response throughput on the same path a client actually uses.

That matters when you are comparing runtimes, not just kernels. A tight inner loop can look excellent while the request path still pays for queueing, transport, and response assembly. Once you measure the full HTTP path, the numbers are harder to hand-wave away, but they are also more useful for capacity planning and for spotting where latency is really coming from.

Internal measurements still have a place. They help isolate decode cost, scheduler overhead, and serialization work. But the user-facing claim should come from the user-facing path. Otherwise you end up optimizing a component and calling it a product result.

What is the first thing you add when you want a benchmark to reflect real user wait time instead of a convenient inner loop?

Engineering fact: Lizard's native readiness benchmark sends concurrent requests through the OpenAI-compatible HTTP endpoint and measures full-response throughput instead of quoting only an internal worker loop.

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#inference #benchmarking #httppath #latency #localai

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