Earlier this week, we looked at running a large language model locally using Ollama. That approach works, but its limitations become obvious once you move beyond experimentation.

Running Ollama on your own machine ties everything to local hardware and a single-user workflow. Your laptop becomes the “brain,” which constrains model size, limits throughput, puts uptime on you, and makes integration into real applications or team usage fragile.

Hosting your own AI model on a server changes the operating model entirely. Instead of a personal tool, you get an AI system that behaves like infrastructure. The reasons this often makes sense are:

1. More capable hardware
   Servers can use larger, dedicated GPUs with more memory, enabling bigger models, higher context windows, and faster inference (fancy word for "generating") than most personal machines can support.
2. Multi-user and application access
   A hosted model can serve multiple users or applications at the same time, turning it from a personal assistant into a shared service.
3. Easier integration into real systems
   Exposing the model as a stable HTTP API makes it straightforward to connect to backends, pipelines, and production workflows without workarounds.
4. Better security and privacy
   You keep control over prompts, data, logging, and model behavior, without sending requests to a closed, external API.

Platforms like Hugging Face provide the models, tooling, and infrastructure needed to load and serve open-source models on that server. You still interact with the model through a familiar chat or text-generation interface, but with full control over performance, scaling, and deployment.

Beyond hosting, Hugging Face also serves as a central reference point for current AI research and experimentation, offering access to state-of-the-art models and datasets for training or fine-tuning your own systems.

Feel free to contact me if you would like to learn more about how AI might help your business or if you would like to discuss a project.