Iteration in Continuous Batching

General Process of Continuous Batching

Example of Interleaving Prefilling and Decoding in Continuous Batching

Overhead of Dynamic Batch Reorganization in Continuous Batching

Memory Fragmentation in LLM Inference

Prefilling-Prioritized Strategy in Continuous Batching

Simple Iteration-level Scheduling

Priority-Based Scheduling in LLM Inference

Custom Priority Policies in LLM Scheduling

Disaggregation of Prefilling and Decoding using Pipelined Engines

Comparison of Continuous (Prefilling-Prioritized) vs. Standard (Decoding-Prioritized) Batching

LLM Inference Scheduling Strategy

An LLM inference server is processing a batch of three long-running requests. In the middle of this process, after several computational steps have already been completed for the initial batch, a new, short request arrives. How would a system implementing continuous batching most likely handle this new request in the next computational step?

An LLM inference system is designed to maximize hardware utilization. Which of the following operational descriptions best illustrates the core principle of continuous batching, distinguishing it from a static batching approach?

Prefilling-Prioritized Strategy in Continuous Batching

Decoding-Prioritized Strategy in Standard Batching

Custom Priority Policies in LLM Scheduling

Inference Scheduling Trade-offs

An AI company operates a service that uses a large language model to summarize vast archives of legal documents. The primary business goal is to maximize the total number of documents summarized each day. The system receives a constant stream of new summarization requests. Given this primary goal, which scheduling approach for managing inference tasks would be most effective?

Optimizing a Hybrid LLM Service