Examples
Worked examples
- Is an instance
A model card declaring '70B parameters (dense)'.
- Is an instance
A MoE model card declaring '8x22B = 141B total, ~39B active per token'.
Counter-examples
Looks similar, but isn't
- Not an instance
A tokenizer vocabulary size.
- Not an instance
An embedding-table row count alone.
Editorial commentary
Parameter count is the total number of learnable scalar weights in a machine-learning model, conventionally reported as a shorthand count such as “7B” (7 x 10^9 parameters). It is one of the most basic disclosed metadata fields for a model, alongside architecture family and training-data description, and is frequently used — imperfectly — as a rough proxy for a model’s capacity or expected capability.
Why it is a weaker proxy than it looks
Parameter count alone does not determine capability: a mixture-of-experts architecture may have a large total parameter count while only activating a small fraction of those parameters per query, making raw parameter count a poor comparator against a dense model of similar “active” size. Training-data quality and quantity, and increasingly inference-time compute for reasoning-style architectures (see inference), can matter as much as, or more than, parameter count for a given task. Reporting parameter count alongside training compute (FLOPs) and architecture type gives a materially more informative picture than parameter count alone.
Why it matters for research and procurement
Parameter count is one of the few model-scale figures that vendors of closed, API-served models routinely decline to disclose, which is itself worth noting in a methods section or procurement record: “parameter count not disclosed by vendor” is a real limitation to record, not an omission to paper over.
References
Also known as
model size (parameter count sense)
Machine-readable encodings
Use in your systems
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