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Your speech model runs faster than real time. Why do users still wait or hear gaps?

Work two audio timelines: input accumulation before transcription and playback starvation after synthesis. Compare real-time factor, response delay and safe concurrent capacity without mixing their units.

Updated Sep 2026 · Grounded in real AI infrastructure interview loops and written to a senior-engineer editorial bar, with every number worked and every diagram hand-built.

Work two audio timelines: input accumulation before transcription and playback starvation after synthesis. Compare real-time factor, response delay and safe concurrent capacity without mixing their units.

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The concepts behind this question

Ranked by how closely each one overlaps this question's topic, so the first card is the thing to read if the answer above moved too fast.

Advanced
📐 AI Systems Design🔒 Premium
Request Routing and Load Balancing for LLMsRoute LLM requests by eligibility, estimated waiting time and usable prefix state. Work through cache-affinity tradeoffs, stale telemetry, replica restarts and tenant isolation.
Core
🚀 Inference & ServingSign in
Continuous BatchingContinuous batching changes which requests run between model iterations. Learn how a waiting request joins, why an empty slot is not enough, and how to balance throughput against each user’s token latency.
Advanced
🚀 Inference & Serving🔒 Premium
Multi-LoRA ServingA LoRA adapter is a few hundred megabytes of low-rank matrices that turn a base model into a fine-tuned variant, and multi-LoRA serving runs hundreds of them on one copy of the base weights by keeping the adapters in memory and applying the right one per request inside the batch. It is how a platform serves a thousand customers' fine-tunes without a thousand deployments. The costs are an extra small matmul per layer, adapter memory and loading, and a scheduler that has to batch across adapters without starving any of them.
Advanced
🧮 Napkin Math & Capacity🔒 Premium
Capacity Planning and UtilizationCapacity planning for GPUs is deciding how many to have next quarter given that they cost money whether busy or not, that demand arrives in bursts, and that a queue near saturation produces waits that grow without bound. This page works the planning arithmetic for a serving fleet (peak demand, headroom, the p99 penalty of running hot) and a training platform (job mix, queue wait, the value of a shared pool), and gives the queueing intuition that makes 70% look full. The number that decides everything is utilization, and it has a ceiling set by latency, not by hardware.
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FEDITOR'S NOTE

Evaluate whether the candidate defines audio time versus wall time, distinguishes interim transcripts from final results and tests playback continuity rather than relying on average throughput.

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