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?

Removing Completed Sequences in Continuous Batching

Adding New Requests in Continuous Batching

Maintaining an Unchanged Batch in Continuous Batching

Overhead of Dynamic Batch Reorganization in Continuous Batching

An LLM inference system is processing a batch of user requests. An observer notes the following: At the start of one processing step, the active batch contains requests {A, B, C, D}. Immediately before the next processing step begins, the active batch contains requests {A, C, E}. Based on this observation, what is the most fundamental principle of this system's batch management strategy?

Inference Batch Management Scenario

An LLM inference engine processes requests in iterative cycles. Arrange the following events to show the correct sequence for a single cycle where the active batch of requests is modified.