The Big LLM Architecture Comparison

2025-09-10 · Source: Sebastian Raschka · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Advanced, extended

Summary

This analysis compares 11 large language model (LLM) architectures released or popularized in 2025, including Deepseek v3/R1, Almo 2, Gemma 3, Mistral Small 3.1, Llama 4, Quen 3, Small LM3, Kimmy 2, GPDOSS, Grock 2.55, and GLM 4.5. The discussion highlights architectural differences from the original GPT model, focusing on efficiency improvements like Multi-Head Latent Attention (MLA) in Deepseek v3, and various Mixture of Experts (MoE) implementations. It also covers normalization layer placements, such as Almo 2's post-norm and QK norm, and Gemma 3's sliding window attention. Key comparisons include model size, number of transformer blocks, attention heads, and memory usage, noting trade-offs between model depth, width, and inference speed.

Key takeaway

For AI Scientists and NLP Engineers evaluating LLM architectures for deployment, prioritize models that balance capacity with inference efficiency. Architectures like Deepseek v3/R1 and Quen 3 demonstrate effective strategies such as Multi-Head Latent Attention and Mixture of Experts, which are crucial for managing memory and computational costs. You should investigate how different normalization placements and attention mechanisms impact both training stability and inference performance for your specific use case.

Key insights

Modern LLM architectures prioritize inference efficiency and training stability through diverse attention mechanisms and Mixture of Experts.

Principles

• Memory efficiency is a critical bottleneck for LLM inference.
• Normalization layer placement significantly impacts training stability.
• Mixture of Experts increases model capacity while controlling inference cost.

Method

Architectural comparisons involve analyzing attention mechanisms (e.g., GQA, MLA, sliding window), MoE configurations (number/size of experts), and normalization strategies (pre-norm, post-norm, QK norm).

In practice

• Consider Multi-Head Latent Attention for significant KV cache memory savings.
• Implement Mixture of Experts to scale model capacity without proportional inference cost.
• Experiment with normalization layer placement to stabilize LLM training.

Topics

• LLM Architectures
• Mixture-of-Experts
• Attention Mechanisms
• Normalization Techniques
• Positional Embeddings

Best for: NLP Engineer, AI Scientist, AI Engineer, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Sebastian Raschka.