Design a deployment that serves a trillion-parameter model at a million tokens of context with usable latency.

Define cold and reused-context objectives, verify the checkpoint's real state layout, and size per-rank memory and transfer demand. Caching and phase separation help only when their compatibility, retention and capacity assumptions hold.

Updated Oct 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

Multi-Head Latent Attention and Sparse IndexersLatent attention changes what is stored; sparse indexing changes which positions receive expensive attention; recurrent layers summarize history in state. Learn to budget each mechanism separately, including indexer work, cache replication and hybrid prefix snapshots.

Foundational

📐 AI Systems Design

Multi-Region Serving and FailoverMulti-region serving can improve geographic reach and survive a regional outage. It requires enough remaining capacity and a clear plan for state, routing and degraded service.

Advanced

🚀 Inference & Serving🔒 Premium

Disaggregated Prefill and DecodeSeparate prefill and decode to control phase interference, then budget the KV handoff. A short isolated transfer can still overload a shared path; cancellation stops generation before it necessarily releases transfer buffers.

Foundational

🧮 Open Weights & Serving Engines

Multi-Node Serving TopologiesChoose what to distribute: complete replicas, tensor operations, layers, experts or request phases. Compare rank-local fit, communication dependencies, workload concurrency and failure domains. A node boundary is not a universal performance boundary, and multiple layouts can be combined.

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UP NEXT ON YOUR JOURNEY

Next in this trackThe open-weights model you are deploying is multimodal. What changes about serving it?Next in this trackYour agent product's tool-call success rate fell from 94 to 61 percent after a deploy. Debug it.Next in this trackDesign the evaluation you run against a serving deployment, not against a model.

FEDITOR'S NOTE

Give cold preparation, reused context and generated-token gaps separate objectives. State which cache data must remain resident to meet each one.

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