Local LLMs via Ollama & LM Studio - The Practical Guide

Name: Local LLMs via Ollama & LM Studio - The Practical Guide
Price: 9 USD
Availability: InStock

3h 52m 28s

English

Paid

June 20, 2025

AI assistants like ChatGPT and Google Gemini have become everyday tools. However, when privacy, cost, offline operation, or flexible customization are important, the best solution is to run powerful open language models (LLM) directly on your own computer. In this course, you will learn how to run and use local AI models, such as Llama by Meta, Gemma by Google, and DeepSeek, even on a regular laptop—without clouds, subscriptions, or data leaks.

What you will learn:

Overview of open LLMs: where to find them, how to choose them, and why they are important
Hardware requirements: what you need to run models locally
Model quantization: how to run even "heavy" AI on a regular PC
LM Studio: installation, setup, launch, and use of models
Ollama: a convenient way to manage LLM from the terminal or API
Practice: image processing, PDF document summaries, text generation, few-shot prompting, and more
Integration into your own projects: working with APIs and automation

Who the course is for:

Developers who want to integrate AI into applications
Enthusiasts and students interested in cutting-edge technologies
Those who value privacy and control over their data
Anyone who wants to save on subscriptions and explore the capabilities of modern LLMs

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#	Title	Duration
1	Welcome To The Course!	00:19
2	What Exactly Are "Open LLMs"?	06:28
3	Why Would You Want To Run Open LLMs Locally?	06:53
4	Popular Open LLMs - Some Examples	03:44
5	Where To Find Open LLMs?	04:48
6	Running LLMs Locally - Available Options	07:18
7	Check The Model Licenses!	04:05
8	Module Introduction	01:21
9	LLM Hardware Requirements - First Steps	04:22
10	Deriving Hardware Requirements From Model Parameters	05:35
11	Quantization To The Rescue!	06:51
12	Does It Run On Your Machine?	05:51
13	Module Introduction	02:04
14	Running Locally vs Remotely	01:09
15	Installing & Using LM Studio	03:10
16	Finding, Downloading & Activating Open LLMs	09:05
17	Using the LM Studio Chat Interface	04:54
18	Working with System Prompts & Presets	03:27
19	Managing Chats	02:33
20	Power User Features For Managing Models & Chats	06:29
21	Leveraging Multimodal Models & Extracting Content From Images (OCR)	02:49
22	Analyzing & Summarizing PDF Documents	03:28
23	Onwards To More Advanced Settings	01:53
24	Understanding Temperature, top_k & top_p	06:33
25	Controlling Temperature, top_k & top_p in LM Studio	04:46
26	Managing the Underlying Runtime & Hardware Configuration	04:18
27	Managing Context Length	05:22
28	Using Flash Attention	05:09
29	Working With Structured Outputs	05:30
30	Using Local LLMs For Code Generation	02:36
31	Content Generation & Few Shot Prompting (Prompt Engineering)	05:22
32	Onwards To Programmatic Use	02:26
33	LM Studio & Its OpenAI Compatibility	06:01
34	More Code Examples!	05:05
35	Diving Deeper Into The LM Studio APIs	02:11
36	Module Introduction	01:42
37	Installing & Starting Ollama	02:09
38	Finding Usable Open Models	02:57
39	Running Open LLMs Locally via Ollama	07:44
40	Adding a GUI with Open WebUI	02:13
41	Dealing with Multiline Messages & Image Input (Multimodality)	02:39
42	Inspecting Models & Extracting Model Information	03:32
43	Editing System Messages & Model Parameters	06:02
44	Saving & Loading Sessions and Models	03:36
45	Managing Models	05:43
46	Creating Model Blueprints via Modelfiles	06:23
47	Creating Models From Modelfiles	03:27
48	Making Sense of Model Templates	06:40
49	Building a Model From Scratch From a GGUF File	06:38
50	Getting Started with the Ollama Server (API)	02:13
51	Exploring the Ollama API & Programmatic Model Access	05:19
52	Getting Structured Output	02:57
53	More Code Examples!	04:54
54	Roundup	01:45