How do RNNs and Transformers differ?

In this blog, we will learn about how RNNs and Transformers differ. These are the two main ways a computer reads a sequence like a sentence, and we will learn why one of them reads word by word, why the other reads everything at once, and when to use which one.

We will cover the following:

What both of them are for
What is an RNN?
The problem with an RNN
What is a Transformer?
The key difference in one line
RNN vs Transformer side by side
Let's tabulate the difference
When to use which one?
Summary

I am Amit Shekhar, Founder @ Outcome School, I have taught and mentored many developers, and their efforts landed them high-paying tech jobs, helped many tech companies in solving their unique problems, and created many open-source libraries being used by top companies. I am passionate about sharing knowledge through open-source, blogs, and videos.

I teach AI and Machine Learning at Outcome School.

Let's get started.

What both of them are for

Both RNNs and Transformers are built to handle sequences. A sequence is data that comes in order, like the words in a sentence or the values in a time series.

The goal for both is the same: read a sequence and understand it. But they do it in two very different ways. Let's understand each one.

What is an RNN?

An RNN, or Recurrent Neural Network, reads a sequence one step at a time, carrying a memory of what it has seen so far.

In simple words, it reads word by word, left to right. At each step, it combines the current word with a memory called the hidden state. Then it passes the updated memory to the next step.

   word 1  ->  word 2  ->  word 3  ->  word 4
     |           |           |           |
   memory  ->  memory  ->  memory  ->  memory
   (each step passes its memory to the next)

Here, we can see that an RNN works in order, one step after another. Each step must wait for the previous step to finish, because it needs that memory.

The problem with an RNN

But, here is the catch. The step-by-step style causes two problems.

It is slow. Because each step waits for the one before it, an RNN cannot process the whole sequence at once. This makes training slow.
It forgets long-range information. By the time the RNN reaches the end of a long sentence, the memory of the early words has faded. So it struggles to connect words that are far apart.

   "The cat, which we found last summer near the old house, was ___"

   By the time the RNN reaches the blank, the memory of "cat"
   has faded, so it may struggle to fill it correctly.

Here, we can see the weakness. Long-range links are hard for an RNN. We needed a better solution, and the Transformer was introduced to solve this.

What is a Transformer?

Now that we have learned about RNNs, it's time to learn about Transformers.

A Transformer reads the whole sequence at once and uses attention, which lets every word look directly at every other word.

In simple words, it does not go step by step. It looks at all the words together. Through a mechanism called attention, each word can directly focus on any other word, no matter how far away it is.

   All words are seen together:

   The   cat   was   on   the   mat
    \_____\_____\____/____/_____/
        every word can look directly
        at every other word (attention)

Here, we can see the big change. Because a Transformer sees all words at once, it can process them in parallel, which makes training fast. And because every word can look directly at every other word, long-range links are easy. The word at the end can look straight back at the word at the start.

We have a detailed blog on Encoder vs Decoder in Transformers that explains how the two parts of a Transformer work.

The key difference in one line

An RNN reads a sequence step by step with a fading memory. A Transformer reads it all at once, and every word can look directly at every other word.

A quick note for you

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LLM
RAG
MCP
Agent
Fine-tuning
Quantization

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No need to stop reading - bookmark it and watch later when you get time. Future you will thank you.

Now, let's get back to the topic.

RNN vs Transformer side by side

Let's place both next to each other so that the difference becomes very clear.

        RNN                                 TRANSFORMER

   read one step at a time             read all words at once
   w1 -> w2 -> w3 -> w4                 w1  w2  w3  w4  (together)
   memory passed along                 attention connects all
   sequential, so slower               parallel, so faster
   long-range links fade               long-range links stay strong

Let's tabulate the difference

Let me tabulate the differences between RNNs and Transformers for your better understanding.

Point	RNN	Transformer
How it reads	One step at a time	All words at once
Speed of training	Slow, steps wait for each other	Fast, done in parallel
Long-range links	Fade over distance	Stay strong through attention
Memory of the past	A single hidden state that fades	Direct attention to any word
Cost on long sequences	Grows gently with length	Grows quickly, since every word looks at every word
Main use today	Small or streaming tasks	Most modern language and vision models

To learn Transformer Architecture, Attention Mechanism, and Self-Attention and Multi-Head Attention in depth, and to build a Large Language Model (LLM) from scratch, check out our AI and Machine Learning Program at Outcome School.

When to use which one?

This is the real question. Let's make it very simple.

Use a Transformer for most modern tasks, especially language. Transformers power today's large language models, because they train fast and handle long-range links well.
Use an RNN for smaller tasks, or when data arrives one piece at a time in a stream, or on tiny devices where a Transformer would be too heavy. An RNN can also be lighter on very long sequences, where the Transformer's all-to-all attention becomes expensive.

We have a blog on Flash Attention in LLMs that explains how this attention cost is brought down on modern hardware.

Summary

Let's quickly recap what we have learned.

Both RNNs and Transformers handle sequences, but in very different ways.
An RNN reads step by step and carries a memory, which is slow and forgets long-range information.
A Transformer reads all words at once and uses attention, so every word can look at every other word.
This makes Transformers fast to train and strong at long-range links, which is why they dominate today.
RNNs still help on smaller tasks, streaming data, and tiny devices.

This is how RNNs and Transformers differ, and why Transformers power most modern models.

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That's it for now.

Thanks

Amit Shekhar
Founder @ Outcome School

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