Ravi Mandliya

Exploration of AI accelerators and their impact on deploying Large Language Models (LLMs) at scale. Posts in this series: Primer on Large Language Model (LLM) Inference Optimizations: 1. Background and Problem Formulation Primer on Large Language Model (LLM) Inference Optimizations: 2. Introduction to Artificial Intelligence (AI) Accelerators (this post) In the previous post, we discussed the challenges of Large Language Model (LLM) inference, such as high latency, intensive resource consumption, and scalability issues. Addressing these

Overview of Large Language Model (LLM) inference, its importance, challenges, and key problem formulations. Large Language Models (LLMs) have revolutionized the field of Natural Language Processing (NLP) by enabling a wide range of applications, from chatbots and AI agents to code and content generation. However, the deployment of LLMs in real-world scenarios often faces challenges related to latency, resource consumption, and scalability. In this series of blog posts, we will explore various optimization techniques for