Small Language Models (SLMs) in Production: Running Efficient AI on Edge Devices and Consumer Hardware
Small Language Models (SLMs) in Production is the definitive guide to building, deploying, and scaling efficient AI systems on edge devices and consumer hardware-without sacrificing performance, reliability, or security.
As large language models grow more expensive and resource-hungry, a new generation of engineers is turning to Small Language Models to power real-world applications: offline assistants, embedded AI, mobile apps, IoT devices, and privacy-first enterprise systems. This book shows you exactly how to make SLMs work in production, not just in research demos.
You'll learn how to:
Design and select SLM architectures optimized for latency, memory, and power constraints
Deploy AI models on edge devices, CPUs, mobile phones, and consumer GPUs
Apply quantization, pruning, distillation, and hardware-aware optimization
Build robust inference pipelines for real-world workloads
Monitor, update, and secure models running in constrained environments
Balance cost, accuracy, privacy, and performance at scale
Written for AI engineers, ML practitioners, system architects, and product builders, this book bridges the gap between theory and production reality. Whether you're building offline AI products, optimizing costs, or shipping AI to millions of devices, this is your practical roadmap.
If you want AI that runs anywhere-fast, affordable, and reliable-this book is your competitive advantage.
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