The article outlines strategies for deploying large-scale distributed inference of language models using Kubernetes. It starts by clarifying key concepts such as inference, which involves generating predictions from trained models, and quantization, which reduces model precision to save memory. The author emphasizes the importance of understanding metrics like latency and throughput, which are essential for evaluating model performance. Key latency metrics include Time to First Token (TTFT) and Time Per Output Token (TPOT), while throughput metrics measure the system’s ability to handle requests efficiently. Batching strategies are crucial for optimizing performance. Static batching processes a fixed number of requests for predictable output, while dynamic batching adapts to real-time traffic, balancing latency and throughput. The article highlights challenges in load balancing for large language models (LLMs), particularly when traffic fluctuates. Traditional round-robin methods can waste GPU resources, so it advocates for using additional metrics like GPU memory utilization. Intelligent routing strategies, such as Intelligent Inference Scheduling and Prefill/Decode Disaggregation, aim to reduce serving latency and improve efficiency. The piece also introduces advanced techniques like Wide Expert-Parallelism, which supports very large Mixture-of-Experts models, enhancing throughput by enabling parallel processing across multiple experts. By leveraging these methods, organizations can efficiently deploy massive models while maintaining low latency and high throughput, essential for applications requiring real-time interactions.