Improving Latency and Throughput Performance of Llama 2: Achieve Up to 4X Boost | By Het Trivedi | August 2023

Improving Latency and Throughput Performance of Llama 2: Achieve Up to 4X Boost | By Het Trivedi | August 2023

Introduction:

Welcome to our article on real-world benchmarks for Llama-2 13B! Integrating advanced language models into enterprise applications is a necessity in today’s AI-driven world. However, keeping up with the rapid advancements in generative AI can be a challenge. While managed services like those offered by OpenAI provide a streamlined solution, open-source tools have emerged as an alternative for those who prioritize factors like security and privacy. In this article, we will explore the performance difference for Llama 2 using two different inference methods. We will compare a containerized Llama 2 model served via Fast API with the same model served via Text Generation Inference, an open-source library developed by Hugging Face. Join us as we delve into this comparison and understand the differences between these methods.

Full Article: Improving Latency and Throughput Performance of Llama 2: Achieve Up to 4X Boost | By Het Trivedi | August 2023

Real-world benchmarks for Llama-2 13B

Introduction
In the realm of large language models (LLMs), integrating these advanced systems into real-world enterprise applications is a pressing need. However, the pace at which generative AI is evolving is so quick that most can’t keep up with the advancements.

Using Managed Services vs. Open-Source Tools
One solution is to use managed services like the ones provided by OpenAI. These managed services offer a streamlined solution, yet for those who either lack access to such services or prioritize factors like security and privacy, an alternative avenue emerges: open-source tools.

Building Production-Ready Apps
Open-source generative AI tools are extremely popular right now and companies are scrambling to get their AI-powered apps out the door. While trying to build quickly, companies oftentimes forget that in order to truly gain value from generative AI they need to build “production”-ready apps, not just prototypes.

Performance Difference for Llama 2
In this article, I want to show you the performance difference for Llama 2 using two different inference methods. The first method of inference will be a containerized Llama 2 model served via Fast API, a popular choice among developers for serving models as REST API endpoints. The second method will be the same containerized model served via Text Generation Inference, an open-source library developed by hugging face to easily deploy LLMs.

Comparing Scalability
Both methods we’re looking at are meant to work well for real-world use, like in businesses or apps. But it’s important to realize that they don’t scale the same way. We’ll dive into this comparison to see how they each perform and understand the differences better.

What Powers LLM Inference at OpenAI and Cohere
Large language models require a ton of computing power and due to their sheer size, they oftentimes need multiple GPUs. When working with large GPU clusters, companies have to be very mindful of how their computing is being utilized.

LLM providers like OpenAI run large GPU clusters to power inferencing for their models. In order to squeeze as much performance as possible, they optimize the utilization of these clusters. This includes using custom software like Stable Diffusion which is used to create the image included in this article.

Conclusion
Understanding the performance differences between different inference methods for large language models like Llama 2 is crucial for building production-ready AI applications. While managed services offer convenience, open-source tools can provide more flexibility for those concerned about security and privacy. By delving into the specifics of each method and comparing their scalability, developers and businesses can make informed decisions about which approach best suits their needs.

Summary: Improving Latency and Throughput Performance of Llama 2: Achieve Up to 4X Boost | By Het Trivedi | August 2023

Real-world benchmarks for Llama-2 13B provide insights into integrating advanced language models into enterprise applications. While managed services like OpenAI offer streamlined solutions, open-source tools are an alternative for those prioritizing factors like security and privacy. This article explores the performance difference of Llama 2 using two inference methods: containerized Llama 2 model served via Fast API and the same model served via Text Generation Inference, an open-source library by Hugging Face. These methods are designed for real-world use but have different scalability. The article also delves into the computing power behind LLM inference and the importance of efficient utilization in large GPU clusters.

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