Understand Transformer-based LLM architectures and GPU utilization strategies, and gain hands-on experience with the actual serving process using vLLM. This course covers the entire practical workflow, from building AI system pipelines to monitoring and multi-GPU utilization, and is designed for intuitive understanding through diagrams and practice without complex formulas.
171 learners
Level Basic
Course period Unlimited
Reviews from Early Learners
5.0
WonJune Lee
I am not in the deep learning industry, but I work in the field of computer vision (rule-based). Since my company requires LLM and vision-related deep learning technologies, I have been studying these topics. I have only completed about 40% of the course, but I felt compelled to leave a review now. I have taken many deep learning courses, including those by famous and highly-rated instructors, but I haven't found any course as clean and clear as this one. The best part is the quality of the lecture materials. The instructor recorded every single matrix calculation in Excel, which is incredibly helpful when reviewing. The Python code is also well-commented in many places. The quality of the lectures themselves is excellent; the instructor reminds you of parts you might have forgotten, ensuring you don't miss anything. While most other courses show calculations once or twice and then move on, this course goes through the calculations together until the end, which provides great clarity. It seems like the Q&A is monitored frequently, as I received immediate answers to my questions. The lectures seem to have been filmed this year, so it's great that they include a lot of the latest trends. It feels like this course hasn't gained much word-of-mouth yet, but I highly recommend it to anyone who needs to study these topics.
5.0
Jang Jaehoon
Thank you for the great lecture!
5.0
nova7tr
It's my first time taking the course, and it's very helpful. ^-^
Understanding the encoder-decoder structure and core operating principles of the Transformer model
Understanding the evolution of the latest attention mechanisms, including MHA, MQA, GQA, and MLA
Hands-on practice on how to utilize the vLLM engine, the de facto standard for current AI serving
Monitoring key performance metrics such as TTFT and TPOT in a vLLM serving environment
Design and implementation of multi-GPU architecture utilizing Tensor/Pipeline/Data Parallelism
Understanding the Core Concepts of Agent AI and the Principles of Tool Calling
Experience in building AI system pipelines and monitoring performance from a real-world industry perspective
Understanding the latest LLM trends such as MLA, MTP, and n-grams based on the latest research papers
In the era of autonomous AI Agents,
you can utilize various Agent tools and Public APIs such as OpenAI, Claude, and Codex.
However, in a real service environment, you must also consider
data security, network costs, token costs, and GPU resource management.
Therefore, what is important is
an understanding of the Hybrid AI architecture, which combines Public APIs and self-hosted GPU-based LLMs
according to the situation.
sao cho phù hợp với từng tình huống.
In that case, is using only Public APIs always the best option?
Not necessarily.
These days, many LLMs comparable to public APIs (ChatGPT, Claude, Sonnet, etc.) are being developed both domestically and internationally.
However, knowing and using LLMs well is not easy.
Purchasing expensive GPUs and using LLMs with an understanding of them versus using them without understanding
leads to
a significant difference.
(This is the content for Chapter 3-5. Make sure to take away all the remaining key parameters.)
(This is the content for Chapter 5-4. The evolution of Attention is synonymous with the evolution of LLMs.)
Those who wanted to study Transformer and Attention structures,
but found them difficult due to complex formulas and concepts
Those who have used ChatGPT or generative AI services,
but want to understand the principles of how LLMs actually work
AI engineers who need to understand LLM architecture and GPU environments,
and possess the capability to build and operate actual AI systems
The vLLM service will be hosted on Runpod, but
OpenWebUI and FastAPI will also be running on your local computer.
Therefore, please check if your environment meets the requirements below!
Who is this course right for?
Practitioners who use ChatGPT and generative AI but want to understand how LLMs actually work
Beginners who aim to become AI engineers and want to systematically learn LLM serving and system architecture.
Developers who want to understand Transformer and Attention structures from a practical perspective without complex formulas.
Backend and infrastructure engineers who want to understand GPU optimization and the actual workflow of building AI systems in multi-GPU environments
PMs and planners who want to understand LLM architecture and GPU utilization strategies during the AI service planning and development process.
Need to know before starting?
Understanding of basic Python syntax (variables, functions, conditional statements, etc.)
Basic usage of git
Inflearn Verified
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Answers
4.9
Rating
3
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Hello.
I am a 17-year veteran currently working in the Data & AI field at a large corporation.
Since obtaining my Professional Engineer Information Management certification, I have been creating content to share the knowledge I've gained with as many people as possible.
Nice to meet you. :)
Contact: hjkim_sun@naver.com
All
54 lectures ∙ (14hr 27min)
Course Materials:
All
7 reviews
5.0
7 reviews
Reviews 3
∙
Average Rating 5.0
Edited
5
I am not in the deep learning industry, but I work in the field of computer vision (rule-based). Since my company requires LLM and vision-related deep learning technologies, I have been studying these topics. I have only completed about 40% of the course, but I felt compelled to leave a review now. I have taken many deep learning courses, including those by famous and highly-rated instructors, but I haven't found any course as clean and clear as this one. The best part is the quality of the lecture materials. The instructor recorded every single matrix calculation in Excel, which is incredibly helpful when reviewing. The Python code is also well-commented in many places. The quality of the lectures themselves is excellent; the instructor reminds you of parts you might have forgotten, ensuring you don't miss anything. While most other courses show calculations once or twice and then move on, this course goes through the calculations together until the end, which provides great clarity. It seems like the Q&A is monitored frequently, as I received immediate answers to my questions. The lectures seem to have been filmed this year, so it's great that they include a lot of the latest trends. It feels like this course hasn't gained much word-of-mouth yet, but I highly recommend it to anyone who needs to study these topics.
Hello Wonjune Lee, Thank you for your thoughtful review! I put a lot of thought into improving the quality of the lecture materials so that students could receive meaningful resources and review them effectively even later on. I also spent a lot of time thinking about how to effectively convey operations like Attention. The conclusion I reached was that it shouldn't be explained through formulas alone, nor through simple metaphors, nor just through torch code. Believing that it can only be understood by following the flow visually, I tried my best to explain it using Excel, and I'm glad to hear that it was conveyed well :) I hope you gain great insights from the remaining parts of the course. Keep it up!
Reviews 911
∙
Average Rating 4.9
5
Thank you for the great lecture!
Hello Jang jaehoon, Thank you for the course review 👍 I see you've completed 7%. I hope you enjoy the rest of the lessons and find them very helpful. You can do it!
Reviews 9
∙
Average Rating 4.8
5
It's my first time taking the course, and it's very helpful. ^-^
Hello nova7tr, Thank you for your review. I am glad to hear that it was helpful. I hope you enjoy the rest of the course :)
Reviews 2
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Average Rating 5.0
Reviews 2
∙
Average Rating 5.0
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