AI self hosted, privacy gains (potential Beyond Monopolies episode)


Artificial Intelligence (AI) is often presented like a complex field, the state of the art being impossible to understand, models too large to train, incredible work in progress moving forward that could change anything, yet a black box inscrutable for anyone except the selected few.

This is truly damaging to the field as it is a fascinating topic and even though indeed nobody can understand it all, we can all benefit from tinkering with it, learning from it and possibly even using it.

It's also often showcased as impractical for a "normal" person with their "normal" computer. Consequently everything must be done in the "cloud", far away from our scrutiny.

This is also damaging as it is simply false. Some very large models are indeed too large to run on a single computers but most, including what was considered the state of the art just a couple of years ago. In fact the trend to scale might be problematic for the entire field.

Regardless of all those limitation the goal here is to showcase that even though not everything can be done on your desktop, a lot can. Composing from that and learning how it works can help to reconsider a potential feeling of helplessness.

Not only can you self-host AI models, use them, adapt them, but there is a whole community and set of tools to help you do so. This movement itself is very encouraging. AI does not have to be a block box. Your digital life does not have to be owned by someone else, even for the state of the art.



Familiarity with self-hosting, e.g Linux command line, see Shell, and containerization, e.g Docker. Ideally familiarity with Python the most popular work in AI is currently often done in that Programming language.


A desktop with a proper graphic card is recommended. Some solutions do not need it all while others need the last generation of GPU. It is also possible to rend such a configuration in the cloud if necessary, while insuring that the cloud provided has terms of services and overall practices aligned with your needs.


Linux desktop with latest generation NVIDIA GPU, Docker installed and running with NVIDIA support.

Used locally

Used non locally but still open

example as tweets


Typical process

  1. discover news about AI
  2. live demo availability, if so try with own data
  3. open-source repository
  4. models availability
  5. image availability, as Docker or replicate (cog)
    1. otherwise make Dockerfile
      1. very often relying on Anaconda3
  6. try locally
  7. if out of memory (e.g OOM error with Torch) look for slimmed down version
  8. if no Web interface available, use Python or NodeJS to make a basic interface
  9. build demo using the Web API, integrating with other tools, e.g WebXR


Constrained model pathfinder

Training followed

  • university training
  • MOOCs
    • AIClass
    • MLClass
    • Coursera
      • Sequences, Time Series and Prediction
      • Natural Language Processing in TensorFlow
      • Convolutional Neural Networks in TensorFlow
      • Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
      • Computational Molecular Evolution
      • Data Cleaning Course
      • Data Science Projects Course
      • Data Visualization Course

To try

See also