Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying LLMs in Enterprise
  • Table Of Contents Toc
LLMs in Enterprise

LLMs in Enterprise

By : Ahmed Menshawy, Mahmoud Fahmy
close
close
LLMs in Enterprise

LLMs in Enterprise

By: Ahmed Menshawy, Mahmoud Fahmy

Overview of this book

The integration of large language models (LLMs) into enterprise applications is transforming how businesses use AI to drive smarter decisions and efficient operations. LLMs in Enterprise is your practical guide to bringing these capabilities into real-world business contexts. It demystifies the complexities of LLM deployment and provides a structured approach for enhancing decision-making and operational efficiency with AI. Starting with an introduction to the foundational concepts, the book swiftly moves on to hands-on applications focusing on real-world challenges and solutions. You’ll master data strategies and explore design patterns that streamline the optimization and deployment of LLMs in enterprise environments. From fine-tuning techniques to advanced inferencing patterns, the book equips you with a toolkit for solving complex challenges and driving AI-led innovation in business processes. By the end of this book, you’ll have a solid grasp of key LLM design patterns and how to apply them to enhance the performance and scalability of your generative AI solutions.
Table of Contents (20 chapters)
close
close
Lock Free Chapter
1
Part 1: Background and Foundational Concepts
7
Part 2: Advanced Design Patterns and Techniques
13
Part 3: GenAI in the Enterprise
19
Index

Performance optimization

The operational viability of connected LLM systems hinges on their ability to deliver responsive, cost-effective performance at scale. As highlighted in a 2024 McKinsey analysis of enterprise AI deployments, organizations report that performance considerations directly impact adoption rates, with systems exceeding 500 ms latency seeing 30–40% lower user retention (McKinsey Digital, 2024). This reality has driven significant innovation in optimization techniques that address the unique challenges of multi-model architectures, where bottlenecks can emerge from model coordination overhead, sequential dependencies, and resource contention.

The performance characteristics of these systems differ fundamentally from single-model deployments. A joint study by Microsoft Research and Carnegie Mellon University identified three primary sources of inefficiency in connected LLM architectures: inter-model communication latency (accounting for 35–50% of...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
LLMs in Enterprise
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist download Download options font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon