You upgraded the serving engine and throughput fell 15 percent. Find the cause.

Verify the throughput definition and service outcomes, compare resolved configurations and execution traces, then isolate supported changes. Quantify capacity replacement correctly and choose rollback from the full release risk.

Updated Sep 2026 · Learn how AI infrastructure works through explanations, worked examples and diagrams, then practise applying it to interview questions.

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Work through the numbers

Change the assumptions in a free calculator, then explain what the result establishes and what still needs measurement.

The concepts behind this question

Use these concept explanations to review the ideas behind this question.

Foundational

🧮 Open Weights & Serving Engines

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.

Advanced

🚀 Inference & Serving🔒 Premium

The Serving Optimization WaterfallTune an LLM deployment one change at a time against one fixed workload, in an order that follows the binding constraint. A measured run went from 789 to 6,568 output tokens per second, and the two biggest wins were not the knobs people usually reach for first.

Foundational

⚡ Kernels & Compilers

CUDA Programming ModelCUDA programs launch GPU kernels from host code. Thread indexing, memory access and stream ordering determine whether the result is correct.

Advanced

⚡ Kernels & Compilers🔒 Premium

FlashAttention InternalsFlashAttention computes exact attention in tiles without storing the full score or probability matrix in device memory. Follow the running softmax state, understand why inputs can still be reread, and distinguish the mechanisms introduced in the first three papers.

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UP NEXT ON YOUR JOURNEY

Next in this trackA model interleaves linear-attention and full-attention layers. What changes about serving it?Next in this trackYou serve a small, a medium and a large open-weights model. How do you route requests between them?Next in this trackYou need a released bf16 model at half the footprint. Walk through quantizing it yourself.

FEDITOR'S NOTE

Scored on reproducible baselines, effective configuration and runtime evidence, interaction-aware experiments, qualified throughput and correct inverse capacity arithmetic.

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