Small Language Models (SLMs) Are Taking Over

As hardware architectures improve, the cost of running gigantic 100B+ models on cloud GPUs has forced a shift in focus: Small Language Models (SLMs) running locally.

The Power of the Edge

Models like Microsoft’s Phi-3 (3.8B parameters) or Google’s Gemma 2 (2B parameters) are proving that with high-quality, synthetic training datasets, smaller models can achieve high reasoning capacity. The benefits are clear: sub-second latency, zero cloud costs, and offline-first capabilities.