Intel's New AI Chip: A Game Changer for Model Training

Intel's new AI chip outperforms Nvidia's offerings by being 50% faster and cheaper for training models.

### Intel's New Xeon Processor: A Game Changer for AI Development
* 🚀 Intel has launched the Xeon Processor and AI accelerator, causing concern for Nvidia due to its **impressive performance on Llama 2 benchmarks**, showing a **50% increase in speed and cost-efficiency**.
* 💡 The benchmarks reveal that the new Intel chip **outperforms Nvidia's offerings**, highlighting the need for a **more diverse ecosystem** beyond Nvidia's CUDA monopoly.
* 🖥 This new architecture allows developers to **easily migrate existing GPU codebases** to Intel's platform, supporting popular libraries like **PyTorch and TensorFlow** without the burden of **CUDA licensing costs**.
* 📊 Intel's processor boasts superior floating-point operation speeds, achieving **two times faster performance** for floating point 8 models and **four times faster performance** for floating point 16 models, enhancing **AI training capabilities**.
* 🌐 The new AI accelerator is designed to handle various data modalities—ranging from **text to video generation**—making it a **versatile option for developers**.
* 🛠 In this video, the focus shifts to **deploying a personal AI assistant** using Meta's Llama 3 model, fine-tuned on **personal YouTube content**, leveraging the new **Intel chip for cost-effective and efficient training**.
* ☁ The process involves setting up an AWS instance dedicated to the **Intel processor**, ensuring the right **AMI and region** are selected for **optimal performance**.
* 💻 The final implementation is streamlined through a **single Python script** that automates the fine-tuning process, enabling users to run their AI models **locally at a minimal cost**.

hello world. It's SJ, and Nvidia is terrified of this new AI chip that Intel just released. It's called the Xeon Processor and the associated AI accelerator. And the reason that Nvidia is so afraid is because when tested out on Llama 2 benchmarks, it performed 50% faster and 50% cheaper when it came to training these models. And the source of that information is St il. A. We can see those benchmarks right here, where they tested it out on a bunch of different multimodal diffusion transformers, and they found that it was 50% faster and 50% cheaper. Nvidia is terrified of that because the whole ecosystem is locked in with Cuda, and there needs to be less of a monopoly. There needs to be more options in the environment. There needs to be more of an availability for developers to build on different chips. Intel has just released that chip, and in this video, we're going to talk about that chip. I'm going to explain a little bit about how it works, and then we're going to deploy our own AI model to our

own infrastructure. Our own AWS infrastructure that allows us to create our own personal assistant. And what we're going to use for that AI is Meta's Llama 3 model, which they recently released a few months ago, and it is still a state-of-the-art in open-source foundation models. And we're going to fine-tune this Llama 3 model on my YouTube videos to make it into a personal assistant that I can use. And we're going to deploy it to my AWS account. And we're going to use all my YouTube videos as training data for that. And all this is going to be trained using the new Intel chip, the new GTI processor. And it's going to be cheaper, and it's going to be faster than NVIDIA. So, let's talk a little bit about this chip that Intel just released. So, H100s are in very, very high demand right now. Everybody wants them. Zuckerberg's, you know, building a million of them. Everybody's building them, and they're in short supply. So, this is a

good alternative to an H100. We can use an AI accelerator as a GPU alternative, and they work with the CPU, which is called Xeon. It's the new generation of Xeon that works together, so no CUDA, no CUDA licensing costs. If you have an existing GPU codebase, you can migrate it in a few lines of code. Py supports it, TensorFlow. We'll talk a little about the libraries here, but essentially, it's very easy to switch between this Intel GAI accelerator and your existing GPU workflow. And it works with one node or thousands of nodes, so it scales, and it's price performant with the H100. It's actually better in terms of cheaper when it comes to training Llama 2 models and Llama 3, which we're going to do in this video. Intel really has a long history of—they literally invented Moore's law, right? Which is the law that computing power doubles every year. And in this case, you can see that floating-point operations, which are used in AI matrix operations.

are two times faster for floating point 8 models and then four times faster for floating Point 16 models. What does that mean? That means different ranges of compute density. How much information are we packing into this stream of neural network processing chipset that we're using? And the most important part here is that it's freedom to scale without vendor lock-in. And Nvidia really has monopolized this market, and we really need to break up the monopoly and allow for a democratized AI ecosystem. And whatever type of application that we're building with this, it's going to work with this system, whether it's 3D Generation, video generation, text, image, audio, any data modality is going to work on the Intel chipset just like it does on the Nvidia chipset. And it's going to be faster, and it's going to be cheaper. This architecture of this new accelerator, it has many more tensor cores, and tensor cores are specifically designed to process the type of computing operation that neural networks use, and that is in a little wrap input times weight at a bias activate.

very simple how it works. This chip is designed for that. So, that's a little bit about the chip. It runs any type of AI model. It runs PyTorch. Let's get right into building something with it because I like doing actionable things with information. So, we know that Intel just released God3. It's a great GPU alternative accelerator. We don't have to use CUDA. It's cheaper. Great. Okay. What do we do with this? The next step is for us to train our model on the cloud. So, let's find a suitable cloud provider, and I'm going to set a timer here to do this in less than 5 minutes. And the first one that you might want to look at is Intel's own developer cloud. And what I've done is I've requested an instance, and it's pending review because I just did it today. But they're pretty fast with acceptance, so you should submit here to try on Body 3. But for now, since we don't have access to the latest preview version on the developer Cloud, we're going to use AWS directly. So, we're

going to run this AWS instance. This is still a way for us to try out the model on Intel hardware and avoid Nvidia for now to see what it's like. So let's get right into how we're going to do this. Okay, so the first thing we're going to do is we're going to launch our new instance, and that instance is going to be dedicated to this Intel processor and to fine-tuning llama 3 on my YouTube videos. So we're going to call it llama 3 finetune intel, and I'm going to now find that in my AWS dashboard under EC2. What we are creating here is a new computing instance, and a lot of cloud providers are trying to make this easier for you, like Vercel and others, but really, we all want to use AWS. We should all be using AWS because it's cheaper, and it's really the source of all these other startups. Heroku comes to mind, and I know it's easier for beginners to start off using other services, but ultimately, we should be using AWS directly. So once we create

a new instance. It's going to ask us what the machine image is, and what I'm going to do is I'm going to try to find that instance type, and I need to be in the right region. I need to be in Us East one to do this properly, so I'm going to find that instance, and I've found the Ami image that I need in the right region. You have to always remember that the region is very important to AWS. If you don't have the right region, then you won't get the right Ami, and I found a bunch of great Amis here. I'm going to use this one, which uses the god, and I'm going to subscribe now to this instance, and that is going to create a new version of that. It's connected to it. I'm going to launch the instance. It's going to create a new key pair, and that is going to authenticate me to this instance, and I'm going to actually proceed without a key pair, and that is now creating my Intel instance, and now

once we have that instance, we can run our code to finetune llama 3 on my YouTube videos. And how do we do that? I asked Chad gb01 preview, of course, to do all that in a single class file of Python. It was able to do that in this file right here. So, it's using the Hugging Face library to fine-tune llama 3 on my YouTube videos by just feeding it a single Google Drive link. And then, it's going to fine-tune on all that data by transcribing it to txt and then using that as a fine-tuning trainer object in the Hugging Face library to create this final version. And all of this is done in a single Python file, and we can run this inside the browser without any worries. Let me just show you exactly how to do that. So, if we go to [Music] instances, if we go to connect in our instance state, we can then connect to that instance in the browser. And once we've connected inside of the browser, then we can just paste in our Python

file and run it. So, we're going to type in Nano, and then we're going to call this script finetuner dopy. And we will copy the code for that into the new file. And now we can run it, and that's now running on our new instance. And it's going to install all the necessary packages that are necessary, and it'll run that script all in one go, thanks to Chad gb01 previews amazing ability to create working python code in one shot. And that lets us train our own AI model, and we can run this locally. We host it on our own instance, and this is very cheap, actually, right? A few bucks a month, and we have our own AI fine-tuned on our own data. Very useful stuff. Thank you for watching this video. Thank you, Intel, for sponsoring this video. I'm proud to be an Intel Ambassador. Check out helpful links in the video description. If you like the video, smash the like button. It really helps promote the video. Hit the Subscribe button, and for now, I got to

keep training my AI model, so thanks for watching. [Music]

Why NVIDIA is Terrified of This New AI Chip

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Intel's newly released Xeon Processor and AI accelerator are causing concern for Nvidia, as they offer 50% faster and cheaper model training on Llama 2 benchmarks.

The chip's performance challenges Nvidia's dominance and aims to provide more options for developers.

The video will explore the chip's functionality and demonstrate deploying Meta's Llama 3 model as a personal assistant using the new Intel chip on AWS infrastructure.

AI accelerators, working with the new generation of Xeon CPUs, offer a cost-effective alternative to H100 GPUs, with easy migration of existing code.

They provide scalable solutions, from one node to thousands, and are more affordable for training Llama 2 and Llama 3 models.

With faster floating-point operations and more tensor cores, these accelerators aim to democratize the AI ecosystem and break Nvidia's market monopoly.

Building an AI Model with Intel's New Chip

Intel's new chip is designed to run any AI model, including PyTorch, and offers a cheaper alternative to GPUs without using CUDA.

The first step is to train the model on the cloud, with Intel's developer cloud and AWS as potential providers.

The process involves launching a new AWS instance dedicated to the Intel processor for fine-tuning the model.

Setting Up an AWS Instance for Fine-Tuning Llama 3

The process begins by selecting the correct instance type and region, with a focus on Us East one for the right AMI image.

After subscribing to the instance and launching it, a key pair is created for authentication, though proceeding without one is also an option.

The final setup involves running a Python script using the Hugging Face library to fine-tune Llama 3 on YouTube videos, with the entire process manageable from the browser.

A script is created using Nano to install necessary packages and run Python code for training an AI model.

The process is cost-effective, with hosting on a personal instance for just a few dollars a month.

The video is sponsored by Intel, and the presenter encourages viewers to like and subscribe for more content.