ttft
AI infra interview questions tagged ttft, across every topic.
8 questions · 3 unlocked for you
Concepts behind "ttft"
Review the concepts used in these questions.
Foundational
Prefill vs DecodeFollow one prompt through its first prediction and later decode steps, then separate GPU work from the wait a user sees. Learn why batch size and context change the likely bottleneck.🚀 Inference & Serving
Foundational
Latency Metrics: TTFT, TPOT and GoodputRead a stream from its timestamps: distinguish first-token wait, individual gaps and average generation pace. Compute goodput with explicit deadlines, denominators and failure accounting.🚀 Inference & Serving
Foundational
Serving Benchmarks That Do Not LieDesign reproducible serving experiments with realistic arrivals, cache state and failure accounting. Interpret latency-qualified throughput and conditional per-rank bounds without turning optimistic kernel estimates into production capacity.🧮 Open Weights & Serving Engines
Core
SLOs for AI SystemsSLOs define the service outcomes users or training teams need. Choose measurable latency, availability and progress targets, then use their error budgets to guide decisions.🩺 Fleet Reliability & ObservabilitySign in
Core
Designing for Latency SLOsA latency budget follows a request through networking, queueing and model execution. Use measurements to find which stage causes slow responses under load.📐 AI Systems DesignSign in
Advanced
Chunked PrefillFollow a long prompt through a shared token budget, including its final partial chunk. Learn why chunking can reduce stream pauses without guaranteeing a fixed token gap or a universal throughput gain.🚀 Inference & Serving🔒 Premium
