LLM Architecture Gallery
sebastianraschka.com
March 15, 2026
8 min read
Summary
The LLM Architecture Gallery compiles architecture figures and fact sheets from significant LLM comparisons. Users can enlarge figures and navigate to corresponding sections using model titles.
Key Takeaways
- The Llama 3 model features 8 billion parameters and utilizes a dense decoder with GQA and RoPE attention mechanisms, focusing on a pre-norm baseline.
- The DeepSeek V3 model has a total of 671 billion parameters, employing a sparse MoE decoder and MLA attention, with a training recipe oriented towards reasoning.
- Gemma 3 is designed with 27 billion parameters and emphasizes local attention, utilizing a sliding-window/global attention strategy for multilingual capabilities.
- The GPT-OSS 120B model maintains an alternating attention structure similar to its 20B counterpart, scaled up for OpenAI's flagship open-weight release.
Community Sentiment
MixedPositives
- The presentation of LLM architectures is visually appealing and reminiscent of the Neural Network Zoo, which effectively showcases different models.
- A modular approach to understanding neural networks could greatly benefit practitioners by bridging theoretical concepts with real-world applications.
Concerns
- There is a desire for more structured information, such as a family tree of LLM evolution, indicating a lack of clarity in the current presentation.
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Source
sebastianraschka.com
Published
March 15, 2026
Reading Time
8 minutes
Relevance Score
69/100
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Why It Matters
This page is optimized for focused reading: quick context up top, a clean summary block, and a direct path to the original source when you want the full story.