LLM vs SLM: Why Smaller is Smarter for Enterprise AI | Cutting Costs & Protecting Privacy

Building AI: Dream to Deploy

In the race for AI dominance, "bigger" is no longer always better. As Large Language Models (LLMs) face skyrocketing training costs—up by over 4,300% since 2020—businesses are pivoting to Small Language Models (SLMs) for specialised, efficient, and secure performance.
This video explores the strategic divide between LLMs and SLMs and why a Personal Intelligence Engine (PIE)—a local, adaptive SLM—is the future of workplace productivity.

What You’ll Learn:

• The Cost Crisis: Why inference accounts for 80–90% of AI’s total cost of ownership, and how SLMs can slash these expenses by 40–70%.

• Speed & Latency: Why SLMs respond up to 5x faster than their larger counterparts, making them ideal for real-time edge deployment.

• Data Sovereignty: How running models locally or on-premise solves the #1 concern for enterprises: Data Privacy and Security.

• The MECW Paradox: Why advertised context windows often fail in reality, and how smaller, task-specific models provide more reliable, grounded results.

• Task-Specific Fine-Tuning: How models like Microsoft’s Phi family match LLM performance in specialised domains like healthcare and finance using a fraction of the power.

Stop overpaying for generalised intelligence. Learn how to implement a Hybrid Tiered Architecture that uses LLMs for discovery and SLMs as the irreplaceable production "agents" for your daily workflows.

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