Boosting the Performance of Large Language Models (LLMs): Techniques to Improve Throughput and Reduc

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 06 | Jun 2024

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p-ISSN: 2395-0072

Boosting the Performance of Large Language Models (LLMs): Techniques to Improve Throughput and Reduce Latency Vivek Gangasani Sr. AI/ML Solutions Architect, Amazon Web Services ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract - Large Language Models (LLMs) have become pivotal in advancing natural language processing tasks, offering unparalleled performance across various applications. However, their extensive computational requirements present significant challenges, particularly concerning throughput and latency. This paper presents an in-depth examination of strategies to enhance the performance of LLMs. It covers model parallelism, hardware acceleration, efficient architectures, quantization, distillation, and innovative prompt engineering techniques. The paper aims to provide a comprehensive overview of the current state-of-the-art methods to optimize LLMs for practical deployment in real-world scenarios.

Introduction The advent of LLMs, such as GPT-4 and BERT, has marked a significant leap in the field of natural language processing (NLP). These models have demonstrated remarkable capabilities in text generation, question answering, translation, and more. Despite their success, the deployment of LLMs in production environments faces substantial challenges due to their high computational costs and latency issues. Enhancing the performance of LLMs is crucial to leverage their full potential effectively. This paper explores various techniques to improve throughput and reduce latency, with a focus on model optimization and prompt engineering.

Techniques to Improve Throughput 1. Model Parallelism 

Data Parallelism: This technique involves distributing the training data across multiple GPUs or TPUs. Each processor works on its subset of the data independently, and gradients are averaged to update the model parameters. This approach scales well with the number of processors, leading to significant improvements in training throughput.

Mathematical Foundation: Let 𝐷 D be the dataset, 𝐵 B the batch size, 𝑛 n the number of processors, and 𝜃 θ the model parameters. Each processor 𝑖 i processes 𝐷 𝑛 nD and computes gradients 𝑔 𝑖 gi. The update rule is: 𝜃 ←𝜃 −𝜂 1𝑛 ∑𝑖 =1𝑛 𝑔 𝑖 θ←θ−ηn1i=1∑ngi where 𝜂 η is the learning rate. 

Tensor Parallelism: In this method, the model's layers or tensors are partitioned across multiple devices. Each device performs computations on a subset of the model parameters. This technique is particularly effective for large models that cannot fit into the memory of a single device.

Implementation: For a weight matrix 𝑊 W in a neural network layer, split it into 𝑛 n parts: 𝑊 =[𝑊 1,𝑊 2,…,𝑊 𝑛 ]W=[W1,W2 ,…,Wn]. Each processor 𝑖 i computes: 𝑦 𝑖 =𝑊 𝑖 𝑥 yi=Wix and the results are aggregated to form the final output.

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