Introduction Continuous Batching for LLM Inference helps AI platforms serve many users efficiently without making every request wait for an entire batch to finish. Unlike traditional static batching, requests can enter and leave the processing batch dynamically as generation progresses. This helps GPUs stay productive, increases throughput, and gives production AI systems better control over... Continue Reading →

Introduction Knowledge Distillation for LLMs helps smaller language models learn useful capabilities from larger and more capable models. Instead of deploying a massive model for every request, teams can use a larger model as a teacher to help create or train a smaller student model for specific tasks. The result can be an AI system... Continue Reading →