Training
Dense Model
A model in which every parameter is used for every token, unlike a sparse Mixture of Experts model that activates only part of the network.
In depth
A model in which every parameter is used for every token, unlike a sparse Mixture of Experts model that activates only part of the network. Its total and active parameter counts are the same. That makes dense models simpler to train, serve and fine-tune, but they cost more compute per token than an MoE model of the same total size. Meta's Llama 3 405B is a dense Transformer, and the paper says Meta chose that design over MoE to maximize training stability.
Primary source: arxiv.org
Example
A model in which every parameter is used for every token, unlike a sparse Mixture of Experts model that activates only part of the network.
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FAQ
Common questions
What is Dense Model?
A model in which every parameter is used for every token, unlike a sparse Mixture of Experts model that activates only part of the network.
Why does Dense Model matter for AI developers?
Dense Model sits in the Training 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 Dense Model?
Developers Digest publishes tutorials and videos that cover Training topics including Dense Model. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Training
DPO (Direct Preference Optimization)
A training technique that aligns language models with human preferences without needing a separate reward model.
Training
Expert Parallelism
A distributed training and inference strategy for Mixture of Experts models where different experts live on different GPUs instead of every device holding a full copy.
Training
Synthetic Data
Training data generated by AI models rather than collected from real-world sources.
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