The transformer architecture, introduced in the 2017 paper “Attention is All You Need” is the backbone of most generative AI model development, including GPT, Claude, and Gemini.
Its self-attention mechanism allows a model to weigh relationships between all tokens in a sequence at once, which makes it more scalable than earlier architectures like RNNs and LSTMs.
That said, “most generative AI models” does not mean all of them. Image and video generation systems relied on a different backbone called U-Net for years, and that has only recently started shifting toward transformer-based designs.
A newer architecture called Mamba is also gaining adoption in production systems where inference speed matters more than raw scale. This article breaks down what each of these architectures does, which models use them, and where each one currently wins or loses.
Table of Contents
What is a Backbone Architecture in Generative AI
A backbone architecture is the core neural network design that processes input data and generates output in an AI model. It is the structural foundation everything else gets built on, including fine-tuning, safety training, and task-specific adjustments.
Three things determine whether an architecture works well as a backbone:
- How it represents data. Text, images, and audio each have different structures, and the architecture has to convert that data into a format the network can process, usually as numerical tokens or patches.
- How it models relationships between data points. A sentence depends on word order, an image depends on spatial layout. The backbone needs a way to capture these dependencies.
- How it scales. An architecture that works on small datasets but breaks down or becomes too slow on large ones cannot serve as a backbone for production-scale models
The Transformer Architecture and the Self-Attention Mechanism
The transformer was introduced in 2017 by Vaswani et al. in the paper “Attention Is All You Need,” published by Google researchers.
Its core innovation is the self-attention mechanism, which lets a model evaluate how every token in a sequence relates to every other token, rather than processing tokens one at a time in order.
How self-attention works
For each token in an input sequence, the model creates three vectors: a query, a key, and a value. The query represents what that token is looking for, the key represents what each other token has to offer, and the value carries the actual information passed forward.
The model compares each token’s query against every other token’s key to calculate an attention score, then uses those scores to weight the value vectors. The result is a representation of each token that accounts for its context in the full sequence.
Multi-head attention
Transformers run self-attention multiple times in parallel, using separate sets of query, key, and value weights. Each of these parallel runs is called an attention head. One head might learn to track grammatical structure, while another tracks topic relevance.
The outputs from all heads are combined, giving the model several independent perspectives on the same sequence rather than a single averaged one.
Positional encoding
Self-attention on its own does not know the order of tokens, since it compares every token to every other token without regard to position.
Transformers solve this by adding positional encoding, a set of values added to each token’s representation that encodes where it falls in the sequence. This is what allows the model to tell “dog bites man” apart from “man bites dog” despite using the same words.
Why Transformers Replaced RNNs and LSTMs
Before transformers, sequence modeling relied mainly on recurrent neural networks (RNNs) and their improved variant, long short-term memory networks (LSTMs).
Both processed sequences one token at a time, in order, carrying a hidden state forward from each step to the next. This design created two specific problems that limited their use as a backbone for large-scale generative AI.
Problem 1: Sequential processing blocked parallelization
An RNN cannot calculate the output for token five until it has finished processing tokens one through four, since each step depends on the hidden state from the step before it.
This made training slow on the hardware available for large models, since GPUs are built to run many calculations in parallel and RNNs could not take advantage of that.
Transformers removed this bottleneck. Self-attention calculates relationships between all tokens in a sequence in a single pass, so training can be parallelized across GPUs.
Problem 2: Long-range dependencies degraded over distance
In an RNN, information from early tokens has to pass through every intermediate step to reach a later token, and it tends to weaken or get overwritten along the way. This is known as the vanishing gradient problem.
LSTMs reduced this issue with gating mechanisms that controlled what information to keep or discard, but the underlying limitation remained: the further apart two related tokens were, the harder it was for the model to connect them.
Which Generative AI Models Use Transformer Architecture
Most major large language models in production today are built on a decoder-only transformer, a variant that generates output one token at a time using only the tokens that came before it. The specific implementation details vary by lab, and not all of them are publicly disclosed.
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GPT (OpenAI)
The GPT family uses a decoder-only transformer trained on a next-token prediction objective, meaning the model learns to predict the most likely next word given everything before it.
OpenAI published full architectural details through GPT-3, including layer count and parameter size. Architecture specifics for GPT-4 and later models have not been publicly disclosed, though OpenAI has confirmed they remain transformer-based.
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Llama (Meta)
Llama is open-weight, so its architecture is fully documented. It uses a decoder-only transformer with several specific modifications: RMSNorm for layer normalization instead of standard LayerNorm, the SwiGLU activation function in place of ReLU, and rotary positional embeddings (RoPE) instead of the original sinusoidal position encoding used in the 2017 transformer paper.
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Gemini (Google)
Google has described Gemini as a natively multimodal transformer, meaning it is trained jointly on text, images, audio, and video from the start rather than having a text model adapted afterward to handle other data types.
Beyond this high-level description, Google has not published the same level of architectural detail that Meta has for Llama.
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Claude (Anthropic)
Claude is built on a transformer-based architecture. Anthropic has not published the detailed architectural specifications for current Claude models, which is consistent with how most closed-weight model providers handle this information.
Diffusion Models and the Shift From U-Net to Diffusion Transformers (DiT)
Image and video generation models did not start with transformers. For years, diffusion models like the original Stable Diffusion relied on a different backbone called U-Net, a convolutional architecture originally designed for image segmentation. That has been changing since 2023.
How diffusion models generate images
Diffusion models work by reversing a noise process. During training, the model learns to predict and remove noise that was deliberately added to an image in steps.
To generate a new image, the model starts with random noise and repeatedly applies this denoising process until a coherent image emerges.
Why U-Net was the original choice
U-Net processes images using convolutional layers, which scan small local regions of an image and build up larger patterns layer by layer. This made it a natural fit for denoising, since removing noise from an image is fundamentally a local, pixel-level operation.
U-Net based models like the early versions of Stable Diffusion produced strong results, but convolutional architectures do not scale with compute and data the same way transformers do.
The shift to Diffusion Transformers (DiT)
In 2023, researchers Peebles and Xie introduced the Diffusion Transformer (DiT), which replaces the convolutional U-Net backbone with transformer blocks.
Instead of scanning local pixel regions, DiT splits an image into patches, treats each patch as a token, and processes the full set of tokens using the same self-attention mechanism used in language models.
The key finding from the original DiT research was that diffusion model quality improves consistently as transformer compute increases, the same scaling pattern already established for language models.
This made DiT attractive for the same reason transformers won out in language: predictable gains from added compute, rather than architecture-specific tuning.
Mamba and State Space Models as Transformer Alternatives
Mamba is not a transformer variant. It belongs to a different architecture family called state space models (SSMs), and it is gaining adoption in production systems where inference speed matters more than raw benchmark scores.
What a state space model does differently
Transformers compare every token against every other token, which is powerful but expensive: the computation grows quadratically as sequence length increases. State space models take a different approach.
They reframe sequence learning as the evolution of a hidden state over time, rather than direct token-to-token comparison. Transformers store information explicitly, while Mamba compresses it into this hidden state, so the cost does not grow quadratically with sequence length the way self-attention does.
Why this matters for inference speed
Even small Mamba-3 models can outperform comparable transformer models on inference speed, with the gap widening as sequence length increases. This matters because the bottleneck for many real-world systems has shifted from training cost to inference cost.
A model that answers faster and cheaper at deployment time has a real advantage, even without topping every training benchmark.
Where transformers still win
State space models are not a strict upgrade. They perform well on long-range sequence modeling, but transformers still perform better on tasks like exact retrieval, where the model needs to recall a specific piece of information precisely from earlier in the context.
Transformer vs Diffusion Transformer vs Mamba: Architecture Comparison
Each architecture covered so far solves a different problem. The table below summarizes how they compare on the factors that matter most for choosing or understanding a generative AI model.
| Factor | Transformer | Diffusion Transformer (DiT) | Mamba / State Space Model |
| Primary use case | Text generation, language models | Image and video generation | Long sequences, latency-sensitive inference |
| Core mechanism | Self-attention across all tokens | Self-attention applied to image patches | Hidden state evolution over time |
| Compute scaling | Quadratic with sequence length | Quadratic with number of patches | Near-linear with sequence length |
| Strength | Long-range context, in-context learning | High-fidelity generation at scale | Fast inference, low memory at long context |
| Weakness | High compute cost at long sequence lengths | High compute cost at high resolution | Weaker at exact retrieval tasks |
| Production examples | GPT, Claude, Gemini, Llama | Stable Diffusion 3, Sora | Jamba and other hybrid models |
Conclusion
The transformer architecture is still the backbone of most AI development solutions, but “most” is doing real work in that sentence.
Image generation converged toward transformers through DiT, while Mamba and hybrid designs are proving that self-attention is not always the most efficient choice for every layer of a model.
The architecture race is not over. It is just getting more specific about which problem each design solves best.
Frequently Asked Questions
Is GPT a transformer model?
Yes. GPT stands for Generative Pre-trained Transformer, and every model in the GPT family uses a decoder-only transformer architecture. It generates text one token at a time, predicting each next token based on everything that came before it.
What is the difference between a transformer and a diffusion model?
A transformer is an architecture built around self-attention, used most commonly for generating text. A diffusion model is a generation process that creates output by reversing a noise-adding process step by step. The two are not mutually exclusive: modern diffusion models like Stable Diffusion 3 use a transformer, called a Diffusion Transformer, as the backbone that performs the actual denoising at each step.
Will Mamba replace Transformers?
Not entirely, at least not based on current adoption patterns. Mamba and other state space models offer faster, cheaper inference at long sequence lengths, but transformers still perform better at exact retrieval tasks. Most production systems in 2026 use hybrid models that combine both, rather than replacing one with the other.
What architecture does Stable Diffusion use?
Stable Diffusion’s earliest versions used a U-Net backbone, a convolutional architecture. Stable Diffusion 3 switched to a multi-modal Diffusion Transformer (DiT), which applies self-attention to image patches instead of using convolutional layers.
Is Mamba faster than Transformers?
For inference at long sequence lengths, yes. Mamba’s computation does not grow quadratically with sequence length the way self-attention does, which makes it faster and cheaper to run at scale. For shorter sequences or tasks requiring precise retrieval, the speed advantage is smaller and transformers can still perform better overall.
What does self-attention mean in simple terms?
Self-attention is a mechanism that lets a model compare every word or token in a sequence against every other token at the same time, rather than reading through them one by one. This lets the model figure out which words are relevant to each other, regardless of how far apart they are in the sequence.
