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LLM Server

Derek Wales

In this course, you’ll dive into the exciting world of GPU servers, the GPU market and Local Language Models (LLMs). Whether you’re a data scientist, developer, or AI enthusiast, this course equips you with the practical skills needed to harness the power of GPUs and LLMs for building intelligent applications including:

  • Getting GPU infrastructure

  • Downloading open source models

  • Running a local LLM Server

  • Designing with Agents

Enroll now

What's inside

Learning objectives

  • How the gpu/llm market affects demand
  • Setting up a virtual machine equipped with a gpu
  • Setting up local llms with lm studio
  • Getting open-source models
  • Running a local llm server
  • Using that llm to power python app
  • Applications and using llm agents

Syllabus

Intro and set up
Large Language Model server setup
Building a local chat application
Using AutoGen
Read more

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Provides practical skills to leverage GPUs and LLMs, which are increasingly important for building intelligent applications and solving complex problems in various industries
Covers setting up a virtual machine with a GPU, which is essential for running computationally intensive LLMs and experimenting with AI models locally
Explores the GPU/LLM market, which helps learners understand the economic factors driving the adoption of these technologies and make informed decisions about infrastructure investments
Uses LM Studio for setting up local LLMs, which may require learners to familiarize themselves with a specific software environment and its dependencies
Teaches how to use LLMs to power Python apps, which develops skills in integrating AI models into existing software projects and building custom applications
Features AutoGen, which is a framework that facilitates the development of LLM applications, but may require additional learning to fully utilize its capabilities

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in LLM Server with these activities:
Review Basics of Virtual Machines
Solidify your understanding of virtual machines, which are fundamental for setting up a GPU server in the cloud.
Browse courses on Virtual Machines
Show steps
  • Review the concepts of virtualization and hypervisors.
  • Practice creating and configuring a basic VM on a cloud platform.
Read 'Programming PyTorch for Deep Learning'
Gain a deeper understanding of the underlying deep learning principles that power LLMs by studying PyTorch.
View Programming PyTorch for Deep Learning: Creating... on Amazon
Show steps
  • Read the chapters covering neural network architectures and training.
  • Experiment with the code examples provided in the book.
Read 'Generative AI with Python and TensorFlow 2'
Gain a deeper understanding of the underlying generative AI principles that power LLMs by studying TensorFlow.
View Generative AI with Python and TensorFlow 2:... on Amazon
Show steps
  • Read the chapters covering generative models and training.
  • Experiment with the code examples provided in the book.
Three other activities
Expand to see all activities and additional details
Show all six activities
Deploy a Simple LLM-Powered Chatbot
Apply your knowledge by building and deploying a basic chatbot using a local LLM server.
Show steps
  • Choose a simple chatbot framework (e.g., using Flask or FastAPI).
  • Integrate your local LLM server into the chatbot application.
  • Deploy the chatbot to a local server or cloud platform.
Write a Blog Post on LLM Agents
Solidify your understanding of LLM Agents by explaining the concepts in a blog post.
Show steps
  • Research different types of LLM Agents and their applications.
  • Write a clear and concise blog post explaining the key concepts.
  • Include examples and use cases to illustrate the benefits of LLM Agents.
Create a Presentation on the GPU Market
Deepen your understanding of the GPU market by creating a presentation summarizing key trends and players.
Show steps
  • Research the current state of the GPU market.
  • Identify key players and their market share.
  • Create a presentation summarizing your findings.

Career center

Learners who complete LLM Server will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
A Machine Learning Engineer works on the practical implementation of machine learning models. This course directly addresses the skills necessary for this role by providing instruction on how to get GPU infrastructure, download open source models, and run a local large language model server. The course also dives into designing with agents. Anyone interested in this role should take this course because it provides a practical approach to implementation. This course is designed to help you apply the latest techniques and build effective applications. Through hands-on experience, you'll develop the capability to integrate language models into your projects. This will also help you to get open source models and make use of them.
See salaries and explore the career path for Machine Learning Engineer
Artificial Intelligence Developer
An Artificial Intelligence Developer requires a strong foundation in both theory and application of AI models. This course provides a good introduction to the practical aspects of deploying and using GPUs and large language models. The curriculum is helpful for those aiming to become an Artificial Intelligence Developer, as it covers setting up GPU infrastructure, downloading open source models, and running a local large language model server. You'll also learn about designing with agents, which is useful when working in the field. The practical nature of the course, with its focus on real-world applications, will enhance your understanding of machine learning technologies, helping you to develop into a more effective Artificial Intelligence Developer.
See salaries and explore the career path for Artificial Intelligence Developer
Data Scientist
A Data Scientist uses analytical and programming skills to extract insights from data. This course provides a good introduction to setting up the necessary infrastructure for working with large language models. A Data Scientist might use these skills to experiment with different models or incorporate them into data pipelines. This course covers key skills like getting GPU infrastructure, downloading open source models, running a local large language model server, and designing with agents. A Data Scientist would find this content useful because it prepares them to work with the technical infrastructure needed for advanced model deployment and experimentation. The course offers a focused and practical approach to understanding and implementing these technologies.
See salaries and explore the career path for Data Scientist
Software Engineer
A Software Engineer develops applications and software systems, and this course helps them incorporate AI capabilities into their work. The course's focus on getting GPU infrastructure, downloading open source models, running local large language model servers, and designing with agents gives practical experience that enhances the work of a Software Engineer. A Software Engineer who wishes to integrate AI will find a step-by-step process for implementation in this course. The hands-on experience with practical tools will enable a Software Engineer to quickly build and deploy systems with embedded AI, making this course a good option for professionals keen to expand into AI development.
See salaries and explore the career path for Software Engineer
Research Scientist
A Research Scientist in the field of AI often needs to implement and test new models and techniques. This course provides skills in setting up and using local large language model servers, which enables a Research Scientist to test and build applications. The course covers GPU infrastructure, downloading open source models, running a local large language model server, and designing with agents. These topics are crucial for experimental research in artificial intelligence. For a Research Scientist, this course provides a practical grounding in the tech stack needed to conduct cutting edge investigations. This course may be particularly useful for those who seek hands-on experience with the latest technologies.
See salaries and explore the career path for Research Scientist
AI Product Manager
An AI Product Manager leads the strategy, design, and development of AI products, and a technical understanding of underlying technologies is helpful for them. This course helps build an understanding of the technical landscape by giving experience with getting GPU infrastructure, downloading open source models, running a local large language model server, and designing with agents. The course's practical focus on implementation enables an AI Product Manager to make more informed decisions about the feasibility and potential of various AI features. This course may be useful for a product manager who wants to understand how AI models are built, allowing them to work more closely with their engineering teams.
See salaries and explore the career path for AI Product Manager
Data Analyst
A Data Analyst uses data to solve business problems, and a foundational understanding of machine learning can be a value-add. This course gives an understanding of how to get GPU infrastructure, download open source models, and run local large language model servers. The course also introduces the concept of designing with agents. A Data Analyst may find this knowledge helpful when working with data that needs to use large language models. While the role is not directly tied to this course, it may be useful for a Data Analyst interested in leveraging AI to analyze unstructured data.
See salaries and explore the career path for Data Analyst
Cloud Computing Engineer
A Cloud Computing Engineer works on the infrastructure that supports cloud-based services. This course introduces concepts related to setting up virtual machines and handling GPU resources, which is a foundational skill for a Cloud Computing Engineer. The course covers getting GPU infrastructure and setting up local large language model servers, which are becoming increasingly common tasks in cloud environments. This course demonstrates the practical aspects of managing and deploying resources, including GPUs, which are important skills for a Cloud Computing Engineer. This course may be helpful to those entering the field.
See salaries and explore the career path for Cloud Computing Engineer
Technical Consultant
A Technical Consultant works directly with clients to implement technology solutions. With its focus on the technical implementation of large language models via GPU infrastructure, this course helps a Technical Consultant understand the details of deploying AI technologies. The course introduces how to get GPU infrastructure, download open source models, and run a local large language model server, among other topics. A Technical Consultant would want to have a hands-on knowledge of this to better assist clients. This course may be useful for consultants entering this specific tech space. The course's content provides a practical understanding of how to use these technologies.
See salaries and explore the career path for Technical Consultant
Solutions Architect
A Solutions Architect designs and plans technical solutions for various business needs. This course helps build an understanding of the practical aspects of setting up and using GPU servers and local large language models. The course covers setting up a virtual machine equipped with a GPU, getting open source models, and running a local large language model server. A Solutions Architect would need a solid grasp of the technical details to construct solutions. This course may provide useful insight and may help a Solutions Architect who works with AI systems.
See salaries and explore the career path for Solutions Architect
Technology Educator
A Technology Educator trains others on technology topics. This course provides a solid grounding in the practical aspects of large language model servers, including setting up GPU infrastructure, downloading open source models, and running local servers. This knowledge would be valuable in developing effective teaching materials and explanations. A Technology Educator who takes this course may be better able to explain and demonstrate these concepts to students or trainees. It provides the practical tools and knowledge necessary for effective education on the topics covered.
See salaries and explore the career path for Technology Educator
Technical Writer
A Technical Writer creates documentation for technical products and services. This course introduces the concepts of local large language model servers, their setup, and methods for use. This course covers getting GPU infrastructure, downloading open source models, running a local large language model server, and designing with agents. These topics are helpful for a Technical Writer who needs to document AI technologies. While it does not directly relate to typical writing tasks, this course provides an understanding of the underlying technology that can be useful for a Technical Writer who works within the AI field. The course may be useful for a Technical Writer who wishes to explore the AI space.
See salaries and explore the career path for Technical Writer
Startup Founder
A Startup Founder needs a variety of skills when building a tech company, and a basic understanding of relevant technologies may be useful. This course will help by introducing concepts related to setting up GPU infrastructure and running local large language model servers. This course covers getting GPU infrastructure, downloading open source models, and designing with agents, all useful for anyone needing to understand AI technology. A Startup Founder who wishes to know more about these topics may find this course helpful. The practical skills taught in this course can provide a better understanding of the technologies that are available.
See salaries and explore the career path for Startup Founder
Business Analyst
A Business Analyst identifies business needs and often uses data, and some understanding of machine learning concepts may be useful. This course covers topics such as setting up GPU infrastructure, downloading open source models, and running local large language model servers. It also explores designing with agents. A Business Analyst with an interest in AI could find this course useful for background information on how these systems function. It may be helpful for a Business Analyst who needs a high level understanding of how AI systems work, despite the fact that their role is not typically technical. The content of the course may help you build important context.
See salaries and explore the career path for Business Analyst
Project Manager
A Project Manager plans and oversees a variety of projects. This course, with its focus on getting GPU infrastructure, downloading open source models, running a local large language model server, and designing with agents, provides a basic understanding of technologies that a Project Manager may be working on. A Project Manager who works on AI projects could perhaps find some value in a course of this type. While the course may not provide directly applicable skills for the role, it may be useful in providing a technical foundation. This course may be helpful to some Project Managers working in AI oriented projects.
See salaries and explore the career path for Project Manager

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in LLM Server.
Cover image
Programming PyTorch for Deep Learning
Save
Provides a practical guide to using PyTorch, a popular deep learning framework. It covers essential concepts and techniques for building and training neural networks. While the course focuses on LLMs, a solid understanding of deep learning principles is beneficial. This book is useful as additional reading to provide more depth to the course.
Programming PyTorch for Deep Learning: Creating and...
Paperback
$$
Programming PyTorch for Deep Learning: Creating and...
Kindle Edition
$$
Cover image
Generative AI with Python and TensorFlow 2
Save
Provides a practical guide to using TensorFlow 2 for generative AI. It covers essential concepts and techniques for building and training generative models. While the course focuses on LLMs, a solid understanding of generative AI principles is beneficial. This book is useful as additional reading to provide more depth to the course.
Generative AI with Python and TensorFlow 2: Create...
Paperback
$$
Generative AI with Python and TensorFlow 2: Create...
Kindle Edition
$$

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Effort
3 to 6 hours per week for 4 weeks
Level
Introductory
Via
edX
Institution
Pragmatic AI Labs
Instructor
Derek Wales
Session
August 8, 2024 - August 8, 2027
Language
English
Transcript
English
Good to know
Provides practical skills to leverage GPUs and LLMs, which are increasingly important for building intelligent applications and solving complex problems in various industries
Covers setting up a virtual machine with a GPU, which is essential for running computationally intensive LLMs and experimenting with AI models locally
Explores the GPU/LLM market, which helps learners understand the economic factors driving the adoption of these technologies and make informed decisions about infrastructure investments
Uses LM Studio for setting up local LLMs, which may require learners to familiarize themselves with a specific software environment and its dependencies
Teaches how to use LLMs to power Python apps, which develops skills in integrating AI models into existing software projects and building custom applications
Features AutoGen, which is a framework that facilitates the development of LLM applications, but may require additional learning to fully utilize its capabilities
Share and help others discover this course.
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