3 tips for navigating the AI swarm - 4M models and counting (www.zdnet.com)

Hugging Face now hosts roughly 4 million open-source AI models—built and used by a community of about 10 million developers—with a new model published roughly every five seconds. That explosion reflects a broader shift: AI is following the open-source pathway of operating systems and developer tools, enabling low-cost experimentation, rapid switching between solutions, and a proliferation of specialized models for everything from neural nets to risk management. But the sheer volume creates discovery and quality-control problems: models are distributed “as-is,” often requiring technical expertise to evaluate and integrate, and the flood makes curation by a single platform impractical. Practically, this means different strategies for different needs. For quick, supported deployments, commercial/closed models still win because of funding, customer support, and polish. For narrow tasks, lightweight models are increasingly viable—Hugging Face’s new SmolLM3 (≈3 billion parameters) is an example of an LLM designed to run on laptops and phones, reducing latency and cost. To navigate the “AI swarm,” rely on community signals—social recommendations, adoption trends, and curated blog posts—to filter options, and match model size and architecture to task constraints (compute, latency, privacy), recognizing that open-source gives flexibility at the cost of extra integration and governance work.