Lizard inference engineering: TTFT and completion time diagnose different pain
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Inference Engineering · Day 13 · Evening
One latency number is too blunt for streamed output.
Time to first content tells you when the answer starts. End-to-end response time tells you when it is actually done. Latency per generated token adds a third view: it normalizes a reply so a long completion does not hide a slow decode path behind a fast start.
That separation matters in practice. A slow first token can point to loading, prompt setup, or transport. A slow finish with a decent first token usually shifts attention to decode, token pacing, or work that accumulates after the stream has already begun. If you compress both into one figure, you lose the shape of the delay and the place to look next.
Lizard records time to first content, end-to-end response time, and latency per generated token on native HTTP and Chat turns, so those cases stay visible instead of being averaged away. That makes it easier to compare a request that feels responsive at the start with one that reaches the user too late.
In a real system, which symptom tends to show up more often for you: slow first content or slow completion?
Engineering fact: Lizard records time to first content, end-to-end response time, and latency per generated token for native HTTP and Chat turns.
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#LLMObservability #Latency #StreamingAI #DeveloperTools #Performance
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