LLM Quantization for Deployment

This course is a comprehensive, six-part practice test series designed to test and deepen your understanding of LLM quantization — the essential skill of shrinking large language models so they run faster and cheaper without sacrificing too much quality.

Whether you're an ML engineer preparing to deploy models on limited hardware, a data scientist studying for an interview, or simply curious how techniques like GPTQ, AWQ, and GGUF actually work under the hood, this course gives you 600 carefully written multiple-choice questions with detailed explanations for every answer.

You'll work through six tests, each focused on a distinct part of the quantization pipeline:

1. Quantization Fundamentals & Core Concepts — what quantization is, why it matters, and the core tradeoffs involved

2. Quantization Techniques Deep Dive — GPTQ, AWQ, GGUF/llama.cpp, and bitsandbytes compared

3. Precision Formats & Numerical Representations — FP32, FP16, BF16, INT8, INT4, and more

4. Quantization-Aware Training & Calibration — how to recover accuracy lost during precision reduction

5. Deployment & Inference Optimization — serving a quantized model with tools like vLLM and llama.cpp

6. Evaluation, Benchmarking & Production Best Practices — standardized benchmarks, bias audits, and responsible deployment

Every question includes a thorough explanation of both the correct answer and why the other options fall short, so you're building real understanding, not just memorizing facts. This isn't a coding course — no implementation is required — making it ideal for reinforcing concepts you're learning elsewhere or for structured interview prep.

By the end, you'll be able to speak confidently about quantization tradeoffs, choose the right technique and precision for a given deployment, and understand what it actually takes to ship a quantized model responsibly.