Scaling Small Language Models: Why SLMs Are Replacing Giants for On‑Device Edge Infrastructure
Table of Contents Introduction The Rise of Edge AI Why Large Language Models (LLMs) Struggle on the Edge Defining Small Language Models (SLMs) Core Techniques for Scaling Down 5.1 Knowledge Distillation 5.2 Quantization 5.3 Pruning & Structured Sparsity 5.4 Efficient Architectures Practical Example: Deploying a 7‑B SLM on a Raspberry Pi 4 Real‑World Deployments and Case Studies Performance Benchmarks & Trade‑offs Security, Privacy, and Regulatory Advantages 10 Future Outlook: From SLMs to Federated LLMs 11 Conclusion 12 Resources Introduction The last few years have witnessed a paradigm shift in natural language processing (NLP). While the public imagination has been captured by ever‑larger language models—GPT‑4, PaLM‑2, LLaMA‑70B—practical deployments are increasingly gravitating toward small language models (SLMs) that can run locally on edge devices such as smartphones, wearables, and industrial controllers. ...