Open-weights model
Also called open weights, open model, open-source llm, local llm
An AI model whose trained parameters are published, so anyone can download and run it on their own hardware.
An open-weights model is one whose weights, the learned parameters, are released publicly for download. Families such as Llama, Mistral, Qwen, Gemma and DeepSeek have released open-weights versions in many sizes. You can run them on your own computer or servers, fine-tune them, and build products on them, subject to each model's licence.
"Open weights" is more precise than "open source". Many open models release the weights but not the full training data or training code, and some licences restrict commercial use or very large companies. Check the licence before building a product.
Running models yourself has clear benefits: data never leaves your infrastructure, you control costs at scale, you can fine-tune freely, and nobody can change or retire the model under you. The trade-offs are that the strongest models are often closed, and self-hosting needs hardware, engineering time and ongoing maintenance. Tools like Ollama, LM Studio and llama.cpp have made running smaller open models on a laptop straightforward.
Example
A hospital wants AI help summarising clinical notes but can not send patient data to an external service. It runs a quantised open-weights model on its own servers, fine-tuned on anonymised notes, so all data stays inside its network.
Common mistake
Assuming an open-weights model is free to run. The download costs nothing, but GPUs, electricity, engineering time, security updates and monitoring do not. For low or irregular volumes, a hosted API is often cheaper overall.