A Visual Tour of Modern LLM Architectures

2026-03-28 · Source: Sebastian Raschka · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Advanced, extended

Summary

A new online LLM Architecture Gallery has been launched, providing a browsable and comparable visualization of approximately 50 large language model architectures. This free web tool, developed by an editorial analyst, consolidates information from various articles and social media posts over the past one to two years, with plans for regular updates. Users can view detailed information for each model, including attention mechanisms, context length, and licensing, alongside hand-drawn architectural figures. The gallery also features a comparison tool to highlight similarities and differences between selected models, drawing data from sources like the Artificial Intelligence Index for performance scores. Key architectural developments highlighted include Grouped Query Attention (GQA), Sliding Window Attention, Multi-Head Latent Attention (MLA), Deepseek Sparse Attention, and the emerging trend of hybrid architectures that combine different attention mechanisms or integrate state-space models like Mamba layers for improved efficiency and context length.

Key takeaway

For AI Architects and Machine Learning Engineers evaluating LLM designs, the LLM Architecture Gallery provides a centralized, visual resource to compare architectural innovations like GQA, MLA, and hybrid attention. Utilize this tool to quickly understand the trade-offs in KV cache size, computational complexity, and performance, informing your decisions on model selection or custom architecture development for specific application needs, especially those requiring longer context windows.

Key insights

The LLM Architecture Gallery offers a visual, comparative resource for understanding diverse LLM designs and their efficiency trade-offs.

Principles

• Efficiency drives architectural innovation.
• Hybrid designs balance performance and cost.
• KV cache size is a critical optimization target.

Method

The gallery visualizes LLM architectures by analyzing config files, technical reports, and code implementations, then hand-drawing figures and extracting key parameters for comparison.

In practice

• Use GQA to reduce KV cache size.
• Implement MLA for better performance than GQA.
• Consider hybrid attention for long contexts.

Topics

• LLM Architectures
• KV Cache Optimization
• Grouped Query Attention
• Sliding Window Attention
• Multi-Head Latent Attention

Best for: AI Scientist, Machine Learning Engineer, AI Architect

Related on AIssential

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• LLMs: A Journey Through Time and Architecture — LLM evolution prioritizes data quality, multi-stage pre-training, and architectural efficiency over sheer model size.
• What I Learned From Implementing LLM Architectures From Scratch (And How to Get Started) — Coding LLM architectures from scratch reveals critical design nuances and optimization strategies for memory and performance.
• A Dream of Spring for Open-Weight LLMs: 10 Architectures from Jan-Feb 2026 — Recent open-weight LLMs prioritize Mixture-of-Experts and hybrid attention for efficiency and performance across diverse scales.
• RedKnot: Efficient Long-Context LLM Serving with Head-Aware KV Reuse and SegPagedAttention — RedKnot optimizes LLM serving by managing KV cache at the head level, recognizing varied utility across attention heads.
• Model-Native Computing Architecture: Envisioning Future System Architecture Through the Lens of Computer Architecture — The Model-Native Computing Architecture proposes ICAM, a six-layer framework, to unify LLM system design through a computer architecture lens.

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