{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/86e60e540c52490da131c72edacbf743\" frameborder=\"0\" width=\"1902\" height=\"1426\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1426,"width":1902,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1426,"thumbnail_width":1902,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/86e60e540c52490da131c72edacbf743-44ddc27009179a75.gif","duration":917.681,"title":"Bridging the Demo to Production Gap in LLMs 🚀","description":"In this video, I dive into the Demo to Production Gap, focusing on how to transform expensive low LLM prototypes into cost-effective, high-performance production systems. I discuss the challenges we face, including raw computational costs, memory issues, and latency, which can stall deployments. I present a structured framework with three layers—model-centric, inference-centric, and system-centric—highlighting techniques like quantization, knowledge distillation, and continuous batching that can significantly improve efficiency and reduce costs. My key takeaway is to shift our mindset from merely increasing model size to optimizing our entire system for efficiency. I encourage you to consider these strategies and how they can be applied to your projects for better performance and cost management."}