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Walk me through how you'd diagnose high latency in an LLM inference pipeline

The OpenAI FDE deep-dive where you're expected to walk the full stack (network, queueing, tokenization, prefill, KV cache, batching, decode) in order, with a measurement at each hop. The triage sequence, with the numbers that tell you which layer is guilty.

Updated Aug 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.

The OpenAI FDE deep-dive where you're expected to walk the full stack (network, queueing, tokenization, prefill, KV cache, batching, decode) in order, with a measurement at each hop. The triage sequence, with the numbers that tell you which layer is guilty.

20 answers per topic instead of 10, plus saved progress and bookmarks · no cardor unlock all 523 remaining answers · ₹2,000 / $25
UP NEXT ON YOUR JOURNEY
Next in this trackDesign an eval suite that catches silent quality regressions when you upgrade the underlying model.Next in this trackYou have a fixed latency and cost budget at scale. Walk me through quantization vs distillation to hit it.Next in this trackWhen do you use long context, RAG, or prompt caching, and what are the failure modes of each?
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