Our session will unveil how Kubernetes-based cloud-native technologies power the transformation of cutting-edge LLMs and diffusion models from lab experiments to massively scalable SaaS services. Key highlights include:
1. Cloud-Native Scaling for AI Inference: Containerized deployment, dynamic scaling, and distributed scheduling on Kubernetes support millions of daily inference requests, with GPU utilization boosted by 40%;
2. Efficiency Breakthroughs in Inference: Through model quantization, distributed parallelism, and caching strategies, we achieved a 60% reduction in LLM inference latency and 35% cost savings for video generation;
3. SaaS Productization Journey: From API design to billing systems, learn how we packaged complex inference technologies into user-friendly services, driving 300% user growth and serving 500+ global enterprise clients;
4. Battle-Tested Solutions: Lessons from multi-model deployment and multi-tenant isolation scenarios, with open-source toolkits and reusable architecture templates for the community.