Categories: AI/ML Research

Static vs. Dynamic vs. Continuous Batching in LLM Inference

In this article, you will learn how static, dynamic, and continuous batching work in LLM inference, and why the differences between them matter at production…

Serving Multiple Users at Once: How Continuous Batching Keeps LLM Inference Efficient

This article is divided into four parts; they are: • The Problem with Static Batching • Code Example of Static Batching • Continuous Batching: Dynamic Scheduling and Ragged Batching • Full Implementation The simplest way to serve multiple requests together is to use static batching, by grouping them into fixed-size…

June 1, 2026

In "AI/ML Research"

Improve performance of Falcon models with Amazon SageMaker

October 12, 2023

In "FAANG"

Asynchronous Verified Semantic Caching for Tiered LLM Architectures

Large language models (LLMs) now sit in the critical path of search, assistance, and agentic workflows, making semantic caching essential for reducing inference cost and latency. Production deployments typically use a tiered static-dynamic design: a static cache of curated, offline vetted responses mined from logs, backed by a dynamic cache…

February 17, 2026

In "FAANG"

AI Generated Robotic Content