# Open Weights

> An open-weights model publishes its parameters for anyone to download and run — unlike API-only models — with licenses from permissive to restricted.

**An open-weights model is one whose trained parameters are published for download — you can run it on your own hardware, fine-tune it, and quantize it — as opposed to API-only models accessible solely through a provider.**

The term exists because "open source" got stretched: weights-available is not recipe-available, and licenses range from genuinely permissive (Apache-2.0/MIT — see [llama.cpp's ecosystem](/tools/llama-cpp)) through custom community licenses with scale or use restrictions. The honest taxonomy: *open weights* (downloadable parameters), *open source* (code/recipe under OSI terms), *open data* (training corpus) — most "open" models clear only the first bar.

Practically, open weights power everything the API economy can't: [self-hosting for privacy and unit economics](/guides/mlops/self-host-vs-api-llm), [fine-tuning](/glossary/fine-tuning) into specialists, [local inference](/guides/comparisons/best-local-llm-tools-2026), and air-gapped deployments. The strategic story of 2024–2026 is the gap to the [frontier](/glossary/frontier-model) narrowing — strong open-weight families (Llama, DeepSeek, Qwen, gpt-oss) now trail the leading edge by months rather than years, which keeps competitive pressure on API pricing everywhere.

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_Source: https://agentscamp.com/glossary/open-weights — Term on AgentsCamp._