#ai architecture

Three key advantages of SLMs Division of labor: Modern AI architecture uses routers to send routine tasks to 7B-parameter SLMs, reserving trillion-parameter LLMs only for complex reasoning. Economic efficiency: For high-volume, repetitive tasks, SLMs can reduce cloud inference costs by up to 90% while providing near-instant latency. Privacy at the edge: Because SLMs can run locally on-device or on-premises, they reduce the data leakage risks inherent in sending sensitive telemetry to the public cloud. Large language models (LLMs) are the workhorses of AI, supporting ever more sophisticated capabilities and workflows, and approaching near-human level performance. But sometimes more isn’t always better — it’s just more. Specialized data and limited capabilities are just fine for some workflows. This realization is driving the evolution of small language models (SLMs), rather than one-size-fits-all LLMs. SLMs — coming in the form of domain-specific models, statistical langua