DeepSeek vs. ChatGPT — A Detailed Architectural and Functional Breakdown

Author(s): Veritas AI

Originally published on Towards AI.

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The Large Language Models have changed the face of Natural Language Processing by enabling machines to generate human-like copies of text, translate languages, summarize texts, and perform a multitude of other tasks. Fast advances in large-language-modeling have resulted in the production of surface differences across AI systems in terms of architectures, training methodology, and overall performance.

This article elaborates on two of the leading models, ChatGPT and DeepSeek, in its comparison, focusing on the architectural design, training methodologies, performance, and limitations.

ChatGPT uses a transformer architecture. It is modeled on the GPT series. Currently, the most reconfigurable model is GPT-4-a decoder-only Transformer model that can have billions of parameters all tuned for it.

Transformer Architecture From “Attention is all you need” paper by Vaswani, et al., 2017 [1]

The major architectural features include::

Multi-head self-attention mechanisms: This provides attention to segments of the input sentence all at once, capturing long-range dependencies and contextual relationships.Layer normalization and residual connections: These stabilize the training process and improve gradient flow, allowing the model to scale on deep architectures.Layer normalization and residual connections: These techniques stabilize the training process and improve gradient flow, enabling the model to scale… Read the full blog for free on Medium.

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Published via Towards AI