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Developer finds LLM is not the bottleneck in real-time AI pipeline

By PulseAugur Editorial · [1 sources] ·

A developer building a real-time AI meeting assistant called LiveSuggest discovered that the language model, contrary to expectations, was not the primary bottleneck in their pipeline. While the LLM (GPT-5 mini) had a median time-to-first-token of 1.2 seconds, the speech-to-text transcription and a custom "gating" mechanism for deciding when to generate suggestions introduced more significant latency. The developer opted for a faster LLM over a more intelligent but slower one to meet real-time performance requirements. AI

IMPACT Highlights the importance of optimizing non-LLM components like transcription and gating for real-time AI applications.

RANK_REASON Developer's personal project measuring performance of components in a real-time AI application.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Developer finds LLM is not the bottleneck in real-time AI pipeline

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Developer's personal project measuring performance of components in a real-time AI application.
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product, infra
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50 days old
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · G ·

    I measured every millisecond of my real-time AI pipeline. The LLM was the fast part.

    <p>I'm building LiveSuggest, a real-time meeting assistant. It listens to your call and shows you written suggestions while you're still talking: a clarifying question to ask, a point you forgot to make, the definition of a term someone just dropped. No bot joins the call. Everyt…

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