Artificial Intelligence
Intermediate
Small Language Models in Edge Production
Deploy low-latency, quantized small language models directly on edge devices and local hardware.
Course Overview
Learn model quantization techniques (GGUF, AWQ, ONNX), evaluate local inference runtimes, and deploy high-speed private AI endpoints.
What You Will Learn
Quantize large models into GGUF, AWQ, and ONNX formats for edge devices
Run high-throughput local inference using llama.cpp and vLLM
Optimize memory footprints for low-power ARM and desktop GPUs
Build local, privacy-first AI features without external internet connectivity
Benchmark token generation speeds (tok/s) across hardware configurations
Tools & Technologies Used
llama.cpp
Ollama
ONNX Runtime
Python 3.11
FastAPI
Structured Curriculum
2 Modules · 4 Lessons · 195 Minutes Total
Module 1
Module 1: Quantization Mechanics & Local Runtimes
2 lessons
Module 1: Quantization Mechanics & Local Runtimes
Understand 4-bit/8-bit quantization and configure local engines.
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Quantization Formats: GGUF vs. AWQ vs. ONNX
Evaluate trade-offs between model size, RAM consumption, and output quality.
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Configuring llama.cpp & Ollama Local Servers
Deploy local server endpoints with OpenAI-compatible REST interfaces.
Module 2
Module 2: Edge Integration & Performance Benchmarking
2 lessons
Module 2: Edge Integration & Performance Benchmarking
Integrate local SLMs into desktop apps and measure throughput.
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Python Bindings & Local Async Streaming
Stream generated tokens asynchronously into local user interfaces.
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Benchmarking Throughput & Memory Profiling
Profile GPU VRAM, system RAM, and tokens-per-second performance metrics.
Practical Project & Capstone Outcome
🚀 Capstone Project
Zero-Latency Offline Privacy Assistant
Develop a lightweight, self-contained desktop assistant powered by a 3B quantized SLM that runs 100% offline with zero cloud API dependencies.
Prerequisites
- Python intermediate proficiency
- Basic command-line familiarity
Intended Audience
- Edge AI Engineers deploying models on mobile, desktop, or IoT hardware
- Privacy-conscious Developers building offline-first applications
Instructor Information
K
Kenji Sato
Course Author & Industry Expert
Kenji Sato is an Embedded AI Systems Specialist with over 10 years of experience deploying machine learning models on edge devices.
Frequently Asked Questions
Can I take this course on a standard laptop without a high-end GPU?
Yes! Quantized 3B models run smoothly on standard Apple Silicon (M-series) or Intel/AMD CPUs with 8GB RAM.