The algorithm is the method; the model is the artifact.
An algorithm is a sequence of steps a computer follows. In ML specifically, it refers to the learning procedure — how weights are updated (Stanford CS229; MIT OpenCourseware).
A model is the resulting function: architecture plus learned parameters. You can run the same algorithm on different data to get different models (Google AI Glossary, 2024).
.safetensors, .ckpt, .bin)| Aspect | Algorithm | Model |
|---|---|---|
| Tangible? | No (pure instructions) | Yes (file on disk) |
| Changes during training | Usually fixed | Yes — weights update |
| Reusable | Across datasets | Specific to one training run |
| Size | A few lines to a few thousand | MB to TB |
| Swapping | Easy | Hard (retrain) |
An architecture like "Transformer" is sometimes called a model family — the combination of architecture + weights is the specific model.
Is "the transformer" a model or algorithm? Architecture (a type of algorithm). Specific instances (GPT-4) are models.
Can I use GPT-4's algorithm without the weights? The transformer architecture is public; GPT-4's specific training recipe and weights are not.
Are model weights copyrightable? Legally debated; most labs treat them as proprietary regardless.
Is a model the same as an AI system? Broader — an AI system includes model + inference code + safety filters + UI.
Do open-weight models share algorithms? Usually yes — papers describe the training recipe; weights are released.
What about hyperparameters? Settings tuning the algorithm's behavior for a specific model (covered elsewhere).
Can I combine algorithms? Yes — modern training uses many: AdamW + warmup + mixed precision + gradient accumulation.
Algorithms are the craft; models are the artifacts. Knowing the difference clarifies licensing, reproducibility, and product discussions. More on Misar Blog.
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