LLM Architecture: Making the right choice

How to making the right choices of #LLM architectures in research and real-world applications?

Here’s a deeper look at the four foundational #LLMarchitectures, Understanding the key differences.

1. Decoder-Only Models (GPT, LLaMA)

- Autoregressive design: predict the next token step by step.

- Powering generative applications like chatbots, assistants, and content creation.

Strength: fluent, creative text generation.

Limitation: struggles with tasks requiring bidirectional context understanding.

2. Encoder-Only Models (BERT, RoBERTa)

- Built to understand rather than generate.

- Capture deep contextual meaning using bidirectional self-attention.

- Perfect for classification, search relevance, and embeddings.

Strength: strong semantic understanding.

Limitation: cannot generate coherent long-form text.

3. Encoder–Decoder Models (T5, BART)

- Combine the understanding power of encoders with the generative power of decoders.

- Suited for sequence-to-sequence tasks: summarization, translation, Q&A.

Strength: flexible and powerful across diverse NLP tasks.

Limitation: computationally more expensive compared to single-stack models.

4. Mixture of Experts (MoE: Mixtral, GLaM)

- Leverages a gating network to activate only a subset of parameters (experts) per input.

- Provides scalability without proportional compute cost.

Strength: massive capacity + efficiency.

Limitation: complexity in training, routing, and stability.

Decoder-only models dominate today’s consumer AI (e.g., ChatGPT), but MoE architectures hint at the future, scaling models efficiently without exploding costs.

Encoder-only and encoder–decoder models remain critical in #enterpriseAI pipelines where accuracy, context understanding, and structured outputs matter more than freeform generation.