Inference
Weights
The numerical parameters inside a neural network that are learned during training.
In depth
The numerical parameters inside a neural network that are learned during training. Weights determine how the network transforms input into output. When people say a model has 70 billion parameters, they mean 70 billion weights. Releasing model weights publicly is what makes open-source AI models possible, since anyone with the weights can run inference without depending on an API provider.
Example
In practice, developers reach for Weights when they need the capability described above as part of an AI feature or workflow.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover Inference.
FAQ
Common questions
What is Weights?
The numerical parameters inside a neural network that are learned during training.
Why does Weights matter for AI developers?
Weights sits in the Inference part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Weights?
Developers Digest publishes tutorials and videos that cover Inference topics including Weights. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Inference
Input vs Output Token Pricing
The split pricing model where LLM APIs charge one rate per million tokens you send (input) and a higher rate per million tokens the model generates (output).
Inference
Mixture of Experts (MoE)
A model architecture that routes each input to a small subset of specialized sub-networks ("experts") rather than activating the entire model.
Inference
Speculative Decoding
An inference technique where a small, fast draft model proposes several tokens ahead and the large target model checks them all in one parallel forward pass.
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