Model Weights
Language Models & ArchitectureThe learned numerical parameters that encode everything a trained model knows, the actual output of training and the asset an organization most wants to protect.
Run on Eleveight AI's in-region infrastructure, both the training data and the resulting model weights stay inside Armenian jurisdiction from start to finish, never leaving sovereign boundaries.
Overview
A model's weights are the numbers that define its behavior, the values, often billions of them, that training adjusts until the model performs well. Before training, the weights are essentially random and the model knows nothing. Training is the process of shaping them; the finished set of weights is what the model has learned, and loading those weights into the right architecture is what brings a trained model back to life on any compatible hardware.
How it works
During training, weights are updated step by step as the model learns from data, with each adjustment guided by how wrong its predictions were. Once training finishes, the weights are saved, a large file or set of files that can be distributed and loaded elsewhere. At inference time, those saved weights are loaded into GPU memory, and incoming requests are passed through them to produce outputs. The architecture is the structure; the weights are the substance.
Why it matters
For many organizations the weights are the crown jewel, representing the data, expertise, and compute invested in building a model. Where they are stored and processed is therefore a question of security and sovereignty, not just engineering. Keeping weights on in-region, controlled infrastructure ensures this valuable asset never resides on systems governed by foreign law or beyond the owner's ability to audit.
Use cases
- Storing and serving proprietary trained models
- Protecting model assets under data residency rules
- Versioning and deploying models across environments
- Securing the output of expensive training runs