Design video captioning under compute limits

Q: Design video captioning under compute limits

This question evaluates expertise in ML system design for multimodal large models, covering deployment under compute and GPU memory constraints as well as large-scale retrieval and processing of video captions and embeddings.

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Question

Scenario

You are deploying a multimodal large model that generates captions for videos.

Part A — Deployment under compute / VRAM constraints

The model takes video (frames/audio optional) and outputs a text caption.
You must meet latency/throughput goals while staying within tight compute and GPU memory (VRAM) limits.

Prompt: Describe how you would design the end-to-end system (modeling + serving) to reliably deploy this capability under constrained compute/VRAM.

Part B — Fast retrieval for brand ads + watermarking

Assume you already have:

A caption for each video (possibly multiple captions per video)
An embedding vector per video (or per caption)

A brand advertiser wants to quickly find videos relevant to a query (text and/or example creative) and then apply a watermark to matched videos.

Prompt: How would you design and optimize the retrieval + processing pipeline to make this search and watermarking fast at scale? Include indexing, filtering/ranking, caching, and system trade-offs.

Design video captioning under compute limits

Quick Overview

Scenario

Part A — Deployment under compute / VRAM constraints

Part B — Fast retrieval for brand ads + watermarking

Solution

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