AI is everywhere — and every interaction depends on inference. Today’s reasoning models generate far more tokens, creating new pressure on infrastructure. That’s why we built the Think SMART framework to help enterprises navigate the tradeoffs between accuracy, latency and ROI, so AI factories scale efficiently and deliver meaningful business outcomes. 🔹 𝗦cale and complexity ⚖️ 𝗠ulti-dimensional performance 🏗️ 𝗔rchitecture and software 💸 𝗥OI driven by performance 🌐 𝗧echnology ecosystem and install base AI factories that Think SMART stay ahead and deliver real business impact. 👉 Learn how to apply the framework: https://nvda.ws/4mjlPZk
Great work
Balancing accuracy, latency, and ROI is one of the toughest challenges when scaling AI models, so having a structured approach is key. Curious — which dimension tends to be the trickiest for most companies adopting AI at scale?
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AI isn’t just about scaling infrastructure — it’s also about knowing where to draw the line. The fine line between efficiency and credibility, automation and human judgment, determines whether AI delivers true impact or just more output. I explored this in my latest episode → https://chihchienliu.substack.com/p/episode-49-the-fine-line-of-ai
Love the #SMART breakdown, too often the conversation is only about accuracy or cost. Framing it as a multi-dimensional tradeoff makes the challenges of scaling AI much clearer.
AI is like an upgrade of computers, becoming more intelligent, entering various industries, and constantly adapting
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