Abstract
AI teams are moving to self-hosted inference away from hosted LLMs as fine-tuning drives model performance. The catch is scale, hundreds of variants create long-tail traffic, cold starts, and duplicated stacks. This talk shows how one platform can autoscale multi-model serving with shared base weights, hot-swapped adapters, dynamic loading, and smart eviction. You will see how to hold P95 and P99 while serving hundreds of fine-tuned models at near single-model cost. We focus on Kubernetes, dynamo, vLLM and SGLang as core technologies, and the metrics that matter, such as: tokens per second, model load latency, and cost per 1k tokens.
Speaker
Meryem Arik
Co-Founder and CEO @Doubleword (Previously TitanML), Recognized as a Technology Leader in Forbes 30 Under 30, Recovering Physicist
Meryem is the Co-founder and CEO of Doubleword (previously TitanML), a self-hosted AI inference platform empowering enterprise teams to deploy domain-specific or custom models in their private environment. An alumna of Oxford University, Meryem studied Theoretical Physics and Philosophy. She frequently speaks at leading conferences, including TEDx and QCon, sharing insights on inference technology and enterprise AI. Meryem has been recognized as a Forbes 30 Under 30 honoree for her contributions to the AI field.
