The Real Cost of Running Small Language Models (SLMs) on Edge Devices

This article explores the practical limitations of running small language models (SLMs) on local hardware in 2026. It argues that memory bandwidth—not NPUs—is the primary bottleneck, with additional constraints from thermal throttling and limited RAM. The key takeaway is that while edge AI can be cost-effective and viable for certain workloads, achieving enterprise-grade performance requires specialized hardware beyond consumer devices.