A new chapter in the weekly NVIDIA AI Decoded series, which started last week with an article focusing on some of the most important keys in the world of artificial intelligence. This time we will focus on what is known as LLMs, short for “large language models”which are the engine of conversational robots, also known as chatbots.

One of the best examples of what a chatbot can offer is Chat with RTX, a platform that, as I told you then, is full of possibilities because it can work with Call2 13B INT4 as Mistral 7B INT4, which use 13 billion and 7 billion parameters respectively and are two of the most important language models of artificial intelligence today.

What are LLMs and how do they work on NVIDIA GPUs?

Large language models are deep learning algorithms that have been pre-trained with a large amount of data. Thanks to the training they have received, they are able to do things that would have seemed impossible to us a few years ago, such as identifying and recognizing content, translating it, summarizing it, predicting and generating text, and creating other types of content. , including images and videos.

With this in mind, it is easy to see why we can consider them the brains of chatbots, as they are an intelligent part of this type of platform. Without the LLM, chat rooms wouldn’t exist, there is no more. These large language models do an excellent job of classifying and summarizing data and large amounts of text, and can communicate in any language, from what we humans use to computer code, genetic sequences, and proteinograms.

LLM-based chatbots can communicate with humans in completely natural language, are easy to use, and offer a large number of options. Today, they are used at different levels and markets. Among them, we can highlight, for example, development and coding and scientific research, where they helped developing new drugs and vaccines.

As you might imagine, all of this has a high cost at the computational level, and that’s where NVIDIA RTX GPUs come into play, which have specialized hardware to accelerate AI tasks. These, together with various optimization techniques, made it possible to move platforms such as Chat with RTX locally from a modest GeForce RTX 4060 Mobile and enjoy a good level of performance.

Why are LLMs so important?

We have already partially answered this question in the previous section, because they offer a world of possibilities in very important industries and because they can also be adapted to a wide range of usage scenarios, industries and workflows. That gives them great versatility and also offers a high level of performance and significant efficiency in almost every type of language task.

It is also possible to obtain an LLM much more accurate and fine-tuned linguistic translations, we can develop and discover new treatments, create new user experiences, transcribe and summarize financial results, create images and content, and many other things. Its potential is huge, as I said before, and It changes the way we live and the way we work.

Using a chatbot is as easy as talking to a human, because in the end we have no choice but to communicate with them naturally. This simplicity is what gave it so much valueand is an important part of the revolution that this technology is achieving. The most advanced models and trained with more parameters can give us answers to a huge number of questions.

In the future, LLMs are expected to further expand their capabilities in such important aspects as e.g the ability of logical thinking, reasoning, mathematics and breaking down complex requirements into smaller sub-tasks. The latter is very important, as it allows to better refine the solution of requests that require the completion of various tasks, such as booking a holiday or buying a computer with a certain configuration.

If you stayed want more I encourage you to check out the full article that NVIDIA just published on their official blog.

AI generated cover image.