This article explores the evolving landscape of AI infrastructure, tracing the architectural progression from traditional recommendation systems to modern large language model deployments. It demonstrates how personalization engines have transitioned from batch processing to real-time architectures while investigating the unique challenges posed by LLMs that necessitate specialized infrastructure solutions. The paper presents PagedAttention as implemented in vLLM, a novel approach addressing memory management challenges in transformer models through block-level allocation. By contrasting established recommendation pipelines with emerging LLMOps patterns, it provides insights into common infrastructure solutions that support experimentation, continuous training, and efficient inference across both domains, culminating in a practical implementation guide for serving LLaMA models.
Keywords: LLMOps, PagedAttention, Recommendation systems, inference optimization., machine learning infrastructure
