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?

Memory Fragmentation in LLM Inference

Comparison of Memory Allocation in Standard vs. Paged Attention

Diagnosing Inference Server Failures

An inference server running a large language model processes thousands of text generation requests, each with a different sequence length. The server allocates memory for the key and value vectors of each sequence as a single, contiguous block. After some time, the server begins to fail when trying to allocate memory for new requests, despite system monitoring tools showing that a significant total amount of memory is still free. Which statement best analyzes the most likely reason for these allocation failures?

Drawbacks of Contiguous Memory Allocation for KV Caching