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

Hugging Face now hosts four million open-source AI models — built and used by a roughly 10 million-strong community — with “one new model published every five seconds,” underscoring how open source has exploded in the AI era. The library spans everything from core neural networks to domain tools like risk-management models, and the proliferation is creating both opportunity and overload: organizations can avoid vendor lock-in and rapidly swap models, but must contend with an “as-is” ecosystem that rewards technical chops and experimentation over turnkey ease. Practically, this means a shift toward more efficient, smaller models and emergent open-source hardware: you no longer always need massive LLMs for narrow tasks. Hugging Face’s SmolLM3 (3 billion parameters) is an example of a model designed to run on laptops and even mobile devices, enabling on-device inference and lower-cost deployments. For teams, the trade-offs are clear — commercial models still offer faster, supported paths to production, while open source delivers flexibility and long-term control. Given the sheer volume of models, curation becomes critical: use peer recommendations, community signals, adoption trends and targeted benchmarking to filter candidates rather than relying on platform search alone.