Continuous Batching: Optimize LLM Serving Throughput and Latency
About this lesson
In this video, we dive deep into continuous batching, the industry-standard technique for high-performance LLM serving. You will see exactly how it solves the common problems of static batching—like batch lock-in and wasted GPU cycles—by allowing requests to join and leave the execution batch dynamically. You'll learn how to: Identify the three main limitations of static batching Understand hybrid batching and chunked prefill mechanics Visualize a real-time request pipeline from arrival to completion Efficiently process new request prefills without stalling active decodes Maximize GPU utilization by resizing batches on the fly Timestamps: 0:00 - The problem: Why static batching wastes GPU resources 1:01 - Core concepts: Hybrid batching and chunked prefill 1:43 - Walkthrough: Understanding the simulation layout 2:39 - Decoding phase: Active requests in lockstep 3:32 - Mid-batch arrival: How new requests enter the queue 4:08 - Hybrid iterations: Processing prefill and decode together 5:08 - Dynamic exit: Completing requests without waiting for the batch 7:24 - Recap: The three key advantages of continuous batching Watch this video if you are an AI Engineer building scalable inference APIs, an MLOps professional optimizing serving costs, or a developer interested in the inner workings of frameworks like vLLM and TGI. This video is part of the LLM Engineering and Deployment Certification Program by Ready Tensor. ✅ Enroll Now: https://app.readytensor.ai/certifications/llm-engineering-and-deployment-DAROCXlj About Ready Tensor: Ready Tensor helps AI/ML professionals build and evaluate intelligent, goal-driven systems and showcase them through certifications, competitions, and real-world project publications. 🌐 Learn more: https://www.readytensor.ai/ 👍 Like the video? Subscribe and let us know what other optimization techniques you want us to cover!
DeepCamp AI