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What You'll Learn

Set up and run LLMs locally using Ollama and llama.cpp on your own hardware
without relying on cloud APIs,Choose the right quantization level and tune GPU/CPU settings for the best speed-quality tradeoff,Build real applications on local models — chat interfaces
RAG pipelines
tool calling
and structured output,Deploy local LLMs reliably in production
with monitoring
security
scaling
and disaster recovery in place

Requirements

Basic comfort with the command line and installing software on your own computer. Some familiarity with APIs or basic programming (any language) is helpful but not required — core concepts are explained from the ground up. No prior LLM or machine learning experience needed. A computer with at least 8–16GB RAM is recommended for following along with smaller models; a GPU is helpful but not required.

Description

This practice test series is designed to help you build genuine, working confidence in deploying large language models on your own hardware — using Ollama and llama.cpp — instead of relying on cloud APIs.

Across 6 tests and 600 carefully written multiple-choice questions, you'll work through the full arc of local LLM deployment:

Local LLM Fundamentals & Setup covers the core tradeoffs of local vs. cloud deployment, hardware requirements, the GGUF file format, and getting your first model running.

Ollama Core Usage goes deep on the Ollama CLI, Modelfiles, the REST API, model management, and everyday workflows.

llama.cpp Fundamentals covers building from source, key CLI flags, the llama-server API, GBNF grammars, and how llama.cpp compares to higher-level tools like Ollama.

Quantization & Performance Optimization is where you learn to actually tune your setup — choosing the right quantization level, GPU offloading, threading, batching, and benchmarking methodology.

Building Applications on Local LLMs covers API integration, streaming, RAG, tool/function calling, structured output, and the application-level engineering that turns a model into a real product.

Production & Advanced Deployment covers containerization, security, monitoring, scaling, versioning, disaster recovery, and the mindset shift from "it works when I tested it" to something genuinely reliable.

Every single question — right and wrong answers alike — comes with its own explanation, so you understand the reasoning behind each option, not just which one is correct. This is a hands-on, practical course built for developers and hobbyists who want to actually run and ship something on local models, not just read about the theory.

Who this course is for:

This course is for developers
hobbyists
and privacy-conscious builders who want to run large language models on their own hardware instead of depending on cloud APIs — whether for cost control
data privacy
offline use
or simply curiosity. Its well suited to those with some basic technical comfort (command line
basic scripting) who want a practical
hands-on path through the full local LLM stack: getting a model running
tuning it for real performance
building applications on top of it
and eventually operating it reliably in production. It is not intended for those seeking deep theoretical machine learning or model training content — the focus here is on deployment
tooling
and real-world usage.