Hey Everyone! With companies like Microsoft, Dell, and Lenovo launching AI laptops (e.g., Snapdragon X Elite, Intel Core Ultra with NPUs), I’m curious how this could impact Quest Analytics Pro , your flagship data analysis and visualization software. I use Quest Analytics Pro daily for tasks like automated report generation, real-time dashboards, and predictive modeling. However, some workflows (like natural language queries or large dataset simulations) feel sluggish without cloud offloading. These new laptops promise local AI acceleration via NPUs (Neural Processing Units). Could this mean faster, offline-capable AI features in Quest? For example: Instant NLP-driven queries (e.g., “Show Q2 sales trends in APAC”) without waiting for cloud servers. Real-time anomaly detection in streaming data using on-device ML models. Privacy-sensitive processing for industries like healthcare or finance, where cloud reliance is a compliance risk. Right now, Quest Analytics Pro uses cloud-based AI for heavy tasks. But with NPUs offering 40+ TOPS (tera operations per second), could future versions leverage local AI to reduce latency and costs? I’ve seen Adobe and DaVinci Resolve optimize for NPUs—is Quest considering similar updates? Questions for the community: Has anyone tested Quest Analytics Pro on an AI laptop (e.g., Surface Laptop 6, ThinkPad T14 Gen 5)? Did NPUs improve performance? Are there workarounds to force local AI processing in Quest today? Should Quest prioritize offline AI features, or is cloud still the future? Keen to hear your thoughts—especially if Quest’s team has shared any roadmap hints!

AI Laptops & Quest Analytics Pro – Can On-Device AI Boost Performance?

Hey Everyone!

With companies like Microsoft, Dell, and Lenovo launching AI laptops (e.g., Snapdragon X Elite, Intel Core Ultra with NPUs), I’m curious how this could impact Quest Analytics Pro, your flagship data analysis and visualization software.

I use Quest Analytics Pro daily for tasks like automated report generation, real-time dashboards, and predictive modeling. However, some workflows (like natural language queries or large dataset simulations) feel sluggish without cloud offloading. These new laptops promise local AI acceleration via NPUs (Neural Processing Units). Could this mean faster, offline-capable AI features in Quest? For example:

Instant NLP-driven queries (e.g., “Show Q2 sales trends in APAC”) without waiting for cloud servers.
Real-time anomaly detection in streaming data using on-device ML models.
Privacy-sensitive processing for industries like healthcare or finance, where cloud reliance is a compliance risk.

Right now, Quest Analytics Pro uses cloud-based AI for heavy tasks. But with NPUs offering 40+ TOPS (tera operations per second), could future versions leverage local AI to reduce latency and costs? I’ve seen Adobe and DaVinci Resolve optimize for NPUs—is Quest considering similar updates?

Questions for the community:

Has anyone tested Quest Analytics Pro on an AI laptop (e.g., Surface Laptop 6, ThinkPad T14 Gen 5)? Did NPUs improve performance?
Are there workarounds to force local AI processing in Quest today?
Should Quest prioritize offline AI features, or is cloud still the future?

Keen to hear your thoughts—especially if Quest’s team has shared any roadmap hints!