Scaling LLM Serving: Techniques for Multi-Node Distributed Inference and Model Parallelism
As Large Language Models (LLMs) continue to grow in size and capability, the hardware requirements for serving them have become a significant bottleneck. Moving from training to inference is not a trivial task; while training requires massive compute power, serving demands low latency, high throu...