Optimizing Open Models for Production Grade Inference

Open-source foundation models are rapidly closing the gap with proprietary systems, enabling organizations to build powerful AI applications with greater flexibility and control. However, deploying these models in production introduces a new set of challenges: latency, throughput, scalability, and cost efficiency.In this talk, we'll explore the modern inference optimization techniques that power large-scale AI systems in production. Topics include KV cache optimization, cache-aware routing, prefill/decode disaggregation, speculative decoding, and other emerging approaches used to improve performance and reduce infrastructure costs.Through practical examples and real-world architecture patterns, attendees will gain a deeper understanding of how to run open models efficiently at scale.