Why Attend?

The AI landscape is shifting. While Large Language Models (LLMs) like GPT-4 and Claude deliver impressive capabilities, organizations are increasingly challenged by their high costs, latency, and infrastructure demands in production environments.

Small Language Models (SLMs) combined with quantization techniques are emerging as powerful, cost-effective alternatives that deliver enterprise-grade performance with significantly lower computational overhead.

This webinar explores the efficiency revolution in AI — helping you understand when and why SLMs can outperform larger models, and how quantization makes LLMs leaner without meaningful loss in accuracy. You’ll gain practical insights and a clear decision framework to make smarter architectural choices aligned with your business needs, budget, and deployment constraints.

What Makes This Webinar Different?

🔹 Side-by-side benchmarking of LLMs vs SLMs across real-world tasks — comparing cost, speed, and accuracy.

🔹 Live demonstration of quantization in action, showing dramatic model size reduction with minimal performance impact.

🔹 Proprietary decision framework to help teams confidently select the right model type for their use case.

🔹 Practical, production-focused insights from a practitioner with hands-on experience deploying both LLMs and SLMs in enterprise and edge environments.

🔹 Actionable takeaways you can apply immediately — not just theory.