7 Frameworks for Serving LLMs
<p>While browsing through LinkedIn, I came across a comment that made me realize the need to write a simple yet insightful article to shed light on this matter:</p>
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<p>“Despite the hype, I couldn’t find a straightforward MLOps engineer who could explain how we can deploy these open-source models and the associated costs.” — Usman Afridi</p>
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<p>This article aims to compare different open-source libraries for LLM inference and serving. We will explore their killer features and shortcomings with real-world deployment examples. We will look at frameworks such as vLLM, Text generation inference, OpenLLM, Ray Serve, and others.</p>
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<p>Disclaimer: The information in this article is current as of August 2023, but please be aware that developments and changes may occur thereafter.</p>
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<p><a href="https://betterprogramming.pub/frameworks-for-serving-llms-60b7f7b23407">Click Here </a></p>