LLM Architecture Gallery
sebastianraschka.com311 points by tzury 13 hours ago
311 points by tzury 13 hours ago
This is great - always worth reading anything from Sebastian. I would also highly recommend his Build an LLM From Scratch book. I feel like I didn’t really understand the transformer mechanism until I worked through that book.
On the LLM Architecture Gallery, it’s interesting to see the variations between models, but I think the 30,000ft view of this is that in the last seven years since GPT-2 there have been a lot of improvements to LLM architecture but no fundamental innovations in that area. The best open weight models today still look a lot like GPT-2 if you zoom out: it’s a bunch of attention layers and feed forward layers stacked up.
Another way of putting this is that astonishing improvements in capabilities of LLMs that we’ve seen over the last 7 years have come mostly from scaling up and, critically, from new training methods like RLVR, which is responsible for coding agents going from barely working to amazing in the last year.
That’s not to say that architectures aren’t interesting or important or that the improvements aren’t useful, but it is a little bit of a surprise, even though it shouldn’t be at this point because it’s probably just a version of the Bitter Lesson.
This is amazing, such a nice presentation. It reminds me of the Neural Network Zoo [1], which was also a nice visualization of different architectures.
Lovely!
Is there a sort order? Would be so nice to understand the threads of evolutions and revolution in the progression. A bit of a family tree and influence layout? It would also be nice to have a scaled view so you can sense the difference in sizes over time.
There is https://magazine.sebastianraschka.com/p/technical-deepseek which shows an evolution in deepseek family
> The goal of the proof verifier (LLM 2) is to check the generated proofs (LLM 1), but who checks the proof verifier? To make the proof verifier more robust and prevent it from hallucinating issues, they developed a third LLM, a meta-verifier.
So cool — thanks for sharing! Here’s a zoomable version of the diagram: https://zoomhub.net/LKrpB
Thank you so much! As a (bio)statistician, I've always wanted a "modular" way to go from "neural networks approximate functions" to a high-level understanding about how machine learning practitioners have engineered real-life models.
What tool was used to draw the diagrams?
Interesting collection. The architecture differences show up in surprising ways when you actually look at prompt patterns across models. Longer context windows don't just let you write more, they change what kind of input structure works best.
What's the structurally simplest architecture that has worked to a reasonably competitive degree?
Competitiveness doesn't really come from architecture, but from scale, data, and fine-tuning data. There has been little innovation in architecture over the last few years, and most innovations are for the purpose of making it more efficient to run training or inference (fit in more data), not "fundamentally smarter"
If your definition of "competitive" is loose enough, you can write your own Markov chain in an evening. Transformer models rely on a lot of prior art that has to be learned incrementally.
Not that loose lol.
I’m thinking it’s still llama / dense decoder only transformer.
Darn. I clicked here hoping we were having LLMs design skyscrapers, dams, and bridges.
I even brought my popcorn :(
Would be awesome to see something like this for agents/harnesses
Thank you for the high quality diagrams!
I'm surprised at how similar all of them are with the main differences being the size of layers.
What a great idea and nice execution.
An older post from this blog, the linked article was updated recently: https://news.ycombinator.com/item?id=44622608
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Where are you seeing dense? Most of the larger competitive models are sparse. Sure, the smaller models are dense, but over 30B it's pretty much all sparse MoE.
And there are still plenty of hybrid architectures. Nemotron 3 Super 120B A12B just came out, it's mostly Mamba with a few attention layers, and it's pretty competitive for its size class.
But yeah, these different architectures seem to be relatively small micro-optimizations for how it performs on different hardware or difference in tradeoffs for how it scales with the context window, but most of the actual differentiation seems to be in training pipeline.
We are seeing substantial increases in performance without continuing to scale up further, we've hit 1T parameters in open models but are still having smaller models outperform that with better and better training pipelines.
Looks like this may have received the HN Hug of Death. I'm getting "Too Many Requests" error trying to load the images.
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Thanks! This is cool. Can you tell me if you learnt anything interesting/surprising when pulling this together? As in did it teach you something about LLM Architecture that you didn't know before you began?