Open-Source LLM - Freely Available Language Models to Self-Host

An open-source LLM is a language model whose weights are freely available - you may download them, run them yourself, and adapt them for your own purposes. The opposite is closed models, usable only via a provider's API and whose weights you never get to see. Well-known examples of freely available model families are Llama, Mistral, and Qwen.

Open Source vs. Open Weights

Strictly speaking, the two terms are not the same. "Open weights" means the trained model weights are published, so you can download and run the model. "Open source" in the narrower sense would additionally require the training code and training data to be disclosed. In practice, most freely available models are open-weight models: you get the weights and a license that governs use, but not necessarily the complete training pipeline. Licenses also differ - some allow commercial use without restriction, others tie it to conditions. Before use, it is therefore always worth checking the specific license of the model in question.

Benefits for Self-Hosting and Customization

• Self-hosting possible: Because the weights are available, the model can run as an on-premise LLM on your own or EU infrastructure - sensitive data need not leave the building.
• Adaptable: An open model can be tailored to your tasks, domain language, or data via fine-tuning. With closed API models this is only possible to a limited extent, or not at all.
• Independence: No lock-in to a single provider, no surprise price or model changes. You keep control over which model version runs and for how long.
• Predictable costs: Instead of paying per request, you bear the cost of your own hardware - which at high volume can lower the per-request inference costs.

How It Differs From Closed API Models

Closed models are run as a service: you send a request to the provider's API and receive an answer, without worrying about hardware or operations. That is convenient and often provides very capable models, but it binds you to a provider, sends your data outward, and incurs ongoing usage costs. Open-source models reverse this relationship: more responsibility for hardware and operations, but in return full data sovereignty and adaptability.

What Matters in Operation

Downloading an open model is only the first step. For productive use it needs suitable hardware - usually GPU inference - an appropriate model size for the task, and reliable operation. Freely available models come in different sizes and often in quantized variants that reduce resource demand. The choice depends on how demanding the task is and what hardware is available.

Open-Source LLMs at Elasticbrains

At Elasticbrains we select the right open model for your task, adapt it to your data where needed, and operate it where your data should stay. See what this looks like in practice on our service page Running On-Premise AI.