Dense Model

A Dense Model is a neural network where all parameters participate in every forward pass — contrasting with sparse models (MoE, pruned networks) where only a fraction of parameters are active per input. Dense models are the classic architecture for neural networks: every layer's neurons connect to every neuron in the next layer through fully-connected weights, and every weight contributes to every computation. Dense LLMs include the original GPT-3, GPT-4 (likely dense, though architectures aren't publicly disclosed), all the Llama models (Llama 3 8B, 70B, 405B), Claude models, most Mistral models (Mistral Large, Mistral 7B — note that Mixtral variants are MoE), and most older transformer architectures. Dense models are simpler to train and reason about, but become expensive to serve at scale because every parameter must be loaded and computed for every token. Many frontier-class systems are moving toward sparse architectures (MoE) for inference efficiency. AI governance, AI compliance, and AI risk management programs document architecture in deployment records supporting responsible AI through transparency across enterprise AI environments.

Centralpoint Routes Between Dense and Sparse Models Seamlessly: Oxcyon's Centralpoint AI Governance Platform brokers dense Llama, Claude, and OpenAI models alongside sparse Mixtral, DBRX, and DeepSeek options. Centralpoint meters consumption, keeps prompts and skills on-prem, and embeds chatbots into your portals via one JavaScript line.


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