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Tracing Inference Requests

Connect gateway, queue, model execution and streaming delivery without confusing overlapping spans or GPU activity with user-visible latency.

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RELATED CONCEPTS

Latency Metrics: TTFT, TPOT and Goodput DCGM and GPU Telemetry Serving Benchmarks That Do Not Lie Model Routing and Cascades Inference Autoscaling and Cold Starts

LESSONS THAT TEACH THIS

Inference EngineeringCapstone: investigate an SLO miss and write a usable runbook330 min→Inference EngineeringRoute models before replicas22 min→Inference EngineeringCapstone: a reproducible serving teardown240 min→

PRACTICE THIS IN REAL QUESTIONS

AI Infrastructure System DesignDesign observability for an inference fleet: per-request spans, GPU metrics, and cost attribution per tenant.→LLM Inference & Servingnvidia-smi shows 30 percent utilization on our serving fleet. Is that a problem, and what would you look at instead?→LLM Inference & ServingYour speech model runs faster than real time. Why do users still wait or hear gaps?→LLM Inference & ServingDefine TTFT, TPOT and goodput, and tell me how you would measure each one in production.→GPU Fleet Reliability & ObservabilityTrace one request through an inference platform. What are the spans, what does each cost, and where does latency actually hide?→Coding for InfraImplement a bounded request log with timers that remain correct under concurrency→