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The open-weights model you are deploying is multimodal. What changes about serving it?

Follow media through fetching, preprocessing, encoding and language-model execution. Derive visual positions from the actual processor output, account for encoder placement and separate the caches and latency stages that text-only monitoring misses.

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

Follow media through fetching, preprocessing, encoding and language-model execution. Derive visual positions from the actual processor output, account for encoder placement and separate the caches and latency stages that text-only monitoring misses.

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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.

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.
Advanced
🚀 Inference & Serving🔒 Premium
Tokens, Embeddings and LogitsTrace token IDs, embedding rows and next-token scores. Work out which tensors grow with a batch, when all-position logits waste memory, and why token probabilities do not establish factual confidence.
Foundational
🚀 Inference & Serving
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.
Advanced
🚀 Inference & Serving🔒 Premium
Voice Turns, Barge-In and Playback BuffersVoice interruption must stop unwanted generation and invalidate queued playback. Track turn identity, bound audio in milliseconds, and distinguish generated, submitted and actually played output.
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UP NEXT ON YOUR JOURNEY
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.Next in this trackEvery response must be valid JSON against a schema. How do you serve that at scale?
FEDITOR'S NOTE

Scored on processor-specific token accounting, conditional encoder parallelism, complete multimodal memory/latency and quality-preserving resource limits.

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