Real-time video classification with PaliGemma: architecture patterns for low-latency VLM inference

May 24, 2026 · 1:53 PM UTC ·10 min read · 0 reactions · 0 comments · 38 views

#ai #computervision #softwareengineering

TL;DR · WeSearch summary

The article discusses the development of a real-time video classification system using PaliGemma, a vision-language model by Google. It highlights the significant improvements in processing speed achieved through architectural decisions rather than hardware upgrades. The system operates at approximately 0.8 to 1.2 seconds per frame, making it suitable for live video applications.

Key facts

▪PaliGemma is a 3-billion parameter vision-language model designed for efficient video classification.
▪The system built with PaliGemma processes frames at a speed of 0.8 to 1.2 seconds, significantly faster than previous models.
▪Architectural choices, such as input resolution and model size, contributed to the improved performance of the real-time classification system.

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Original publisher	DEV.to (Top)
Canonical URL	https://dev.to/pasquale_molinaro/real-time-video-classification-with-paligemma-architecture-patterns-for-low-latency-vlm-inference-2aj9
Publication time	Sun, 24 May 2026 13:53:24 +0000
Retrieval time	2026-05-24T14:07:32.717Z
Last seen	2026-05-24T14:07:32.717Z
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Opening excerpt (first ~120 words) tap to expand

try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3931605) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Pasquale Molinaro Posted on May 24 • Originally published at Medium Real-time video classification with PaliGemma: architecture patterns for low-latency VLM inference #computervision #ai #python #softwareengineering In a previous article, we benchmarked three open-source Vision-Language Models on zero-shot object detection and arrived at an uncomfortable conclusion: even the fastest contender, Phi-3.5-vision-instruct, takes 4.45 seconds per frame on an NVIDIA L4.

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