3 articles tagged Foundation Models.
An empirical engineering framework for choosing between RAG, LoRA fine-tuning, and in-context learning based on data velocity, hallucination tolerance, and inference cost.
An architectural exploration of KV-cache memory scaling, attention sinks, StreamingLLM, and Heavy Hitter eviction for serving 128k+ token agent workflows efficiently.
An architectural breakdown of MoE gating functions, auxiliary load-balancing losses, and all-to-all token dispatching across distributed GPU clusters.