How does the technology behind Gemini work? The complete technical story

Imagine asking Gemini a question; within seconds, it doesn't just provide an answer—it comprehends your query, views images, listens to audio, interprets videos, and processes thousands of pages of information simultaneously, ultimately using reasoning to formulate a response.

But the real question is: what exactly lies within Gemini that enables it to perform all these tasks at once?

Gemini wasn't created by training just a single large AI model; an entire technology stack was developed for it. Technologies such as specific architectures, multimodal AI, Mixture of Experts, TPU supercomputers, distributed training, reinforcement learning, and inference-time reasoning work in unison. However, simply combining these technologies wasn't the hardest part. The greatest challenge lay in training an AI system capable of understanding diverse types of information, training across thousands of processors simultaneously, and performing additional computation for reasoning when necessary.

This is where the true technical story of Gemini begins.

What is a Transformer

Let’s start with the Transformer, as understanding it is essential to grasping how Gemini works. A Transformer is a type of neural network architecture capable of understanding the relationships between different parts of a sentence or a piece of information. Its most critical component is "Attention." Simply put, Attention helps the model determine which part of the input requires the most focus at any given moment. Consider the sentence: "Ram gave Mohan the book because he knew him." To understand who "he" refers to, one must analyze the relationships between the other words in the sentence. Attention mechanisms calculate precisely these kinds of relationships. This technology eventually became the foundation for large language models.

Why was Gemini designed as a multimodal AI? If a model only needs to understand text, text tokens suffice. However, if it must also comprehend images, audio, and video, these diverse information types must be converted into representations that the model can understand. This is where Multimodal AI comes into play. "Multimodal" refers to a single AI system capable of handling various types of information. Gemini was designed to process text, images, audio, and video within a unified model architecture. For instance, if you provide Gemini with an image of a physics problem, the image is first converted into a visual representation. If the image contains a written question, the model can process both the visual and textual information simultaneously. Subsequently, the Transformer analyzes the relationships between these representations to generate an answer.

How does Gemini understand audio

In the case of audio, the process goes beyond merely converting speech into text. Gemini's initial architecture utilized features derived from Google's Universal Speech Model (USM) for audio processing. This enabled the model to incorporate information extracted from audio into its multimodal processing workflow.

How is data prepared for training Gemini

The question arises: how was such a vast amount of information fed into the model? This requires data preparation. Gemini's training involved diverse data types, including text, code, images, audio, and video. However, one cannot simply take data from the internet and use it to train the model directly; it may contain low-quality content, duplicate information, or unsafe material. Therefore, processes such as data filtering, quality checks, and deduplication are performed prior to training. Finally, the data is converted into a format usable by the model.

What does a tokenizer do

Regarding text, another crucial technology comes into play: the tokenizer. A tokenizer breaks text down into smaller units that the model can process. For example, instead of treating an entire sentence as a single unit, the tokenizer splits it into multiple tokens. These tokens reach the model represented as numbers. The tokenizer becomes even more critical for multilingual AI; if text in a particular language breaks down into a larger number of tokens, processing that same information requires more computational power and memory.

Why are TPUs essential for training Gemini

The data and the model architecture are ready, but a major challenge remains: who will train such a massive model? This is where one of Google's key technologies comes into play—the TPU, or Tensor Processing Unit. A TPU is a specialized processor developed by Google specifically for machine learning workloads. Its function is to rapidly process the vast number of mathematical operations involved in neural networks. However, a model like Gemini cannot be trained using just a single TPU; it requires linking thousands of TPUs together. Imagine you have a massive book that would take one person ten years to read; if thousands of people work on it together, the task can be completed much faster. Distributed training operates on this very same basic principle.

How does distributed training work

The model and training computations are distributed across numerous accelerators. However, simply dividing the workload isn't enough; these TPUs must constantly exchange information with one another. Managing which processor holds specific data, where the results of calculations need to go, and when to synchronize all processors is essential. Google employs software systems like JAX, XLA, and Pathways for this purpose. JAX facilitates machine learning computations and automatic differentiation; XLA optimizes and compiles these computations; and Pathways helps coordinate such massive distributed systems.

What is "Mixture of Experts"

This brings us to Gemini's next major technical challenge: how to scale up the model without making every calculation unnecessarily expensive. This is where the "Mixture of Experts" (MoE) concept comes into play. Consider a simple example: imagine a company with distinct experts for different tasks—a mathematics expert, a coding expert, a language expert, and a vision expert. It would be inefficient to route every query to every single person in the company. Instead, a manager would assess the nature of the query and direct it to the relevant expert. The "Router" in an MoE architecture functions in much the same way. While the model contains multiple experts, not all of them are activated for every input; the router determines which experts are best suited for that specific input. This allows for a massive total model capacity without requiring full computation for every token. A generation of Gemini utilized this "Sparse Mixture of Experts" architecture to implement this approach at scale.

Long-Context Capability

This generation also saw rapid advancements in long-context capabilities. Imagine the possibilities if a model could maintain a context of millions of tokens rather than just a few thousand. Gemini is capable of handling a context window of up to one million tokens at a time. This means the model can process information from massive documents, extensive codebases, or long videos within a single context. However, a large context window is not inherently useful on its own. The real challenge lies in finding relevant information within that vast amount of data and understanding the relationships between the pieces. This is precisely what makes long-context reasoning technically difficult.

What is Inference-Time Reasoning

Gemini's technology then advanced in another direction: reasoning. A standard model can begin generating an answer immediately after receiving a prompt. However, a reasoning model can be permitted to perform more computation on complex problems. In other words, the model is allocated additional "inference-time compute" to analyze the problem in steps before providing an answer. You can think of this as a "thinking budget": less computation for simple questions and more for difficult ones.

What role does Reinforcement Learning play

This is where Reinforcement Learning (RL) becomes crucial. In RL, it is not enough to simply show the model the correct answer; through reward signals, the model can learn which reasoning path yields better results. This approach is used to enhance the model's reasoning capabilities in areas such as mathematics, coding, scientific problem-solving, and multi-step tasks.

How does Gemini use tools

However, creating a model that merely "thinks" was not the ultimate goal; it also needed to learn how to use tools. For instance, if a question cannot be answered using the model's internal knowledge, it can invoke an external tool like Search. The tool's output is fed back into the model's context; the model reads it, takes further action if necessary, and then formulates the answer. This marks the transition of the language model from a simple answer generator to an agentic system.

Gemini's Training and Post-Training

One constant remains throughout this entire journey. Gemini is first pre-trained on a massive dataset. It is then taught to follow instructions through Supervised Fine-Tuning (SFT). Finally, post-training and reinforcement learning are conducted based on human preferences and reward signals. Knowledge Distillation can also be used for smaller models, where a large "Teacher Model" helps transfer its capabilities to a smaller "Student Model." These techniques enable the model to run with reduced memory and computational requirements.

Gemini's Complete Technical Journey

So, to summarize Gemini's entire technical journey in a single line: it is not merely the story of a neural network. It is a narrative that encompasses multimodal data, Transformer architecture, specialized TPU hardware, distributed training, Sparse Mixture of Experts, long-context processing, reinforcement learning, reasoning, and the use of tools. And the most interesting aspect is that Gemini's technology does not stop there; with every new generation, the training and inference components undergo further optimization. Yet, after understanding all this, one question remains paramount: how exactly was Gemini trained on thousands of TPUs, and how were all those processors made to work in unison while sharing a single model? This is the technical challenge without which creating a frontier AI model like Gemini would be virtually impossible.