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Generative AI: The Future of Creativity, Powered by IPU and GPU

  • By Gcore
  • September 18, 2023
  • 8 min read
Generative AI: The Future of Creativity, Powered by IPU and GPU

In this article, we explore how Intelligence Processing Units (IPUs) and graphics processing units (GPUs) drive the rapid evolution of generative AI. You’ll learn how generative AI works, how IPU and GPU help in its development, what’s important when choosing AI infrastructure, and you’ll see generative AI projects by Gcore.

What Is Generative AI?

Generative AI, or GenAI, is artificial intelligence that can generate content in response to users’ prompts. The content types generated include text, images, audio, video, and code. The goal is for the generated content to be human-like, suitable for practical use, and to correspond with the prompt as much as possible. GenAI is trained by learning patterns and structures from input data and then utilizing that knowledge to generate new and unique outputs.

Here are a few examples of the GenAI tools with which you may be familiar:

  • ChatGPT is an AI chatbot that can communicate with humans and write high-quality text and code. It has been taught using vast quantities of data available on the internet.
  • DALL-E 2 is an AI image generator that can create images from text descriptions. DALL-E 2 has been trained on a large set of images and text, producing images that look lifelike and attractive.
  • Whisper is a speech-to-text AI system that can identify, translate, and transcribe 57 languages (a number that continues to grow.) It has been trained on 680,000 hours of multilingual data. This is a GenAI example in which accuracy is more important than creativity.

GenAI has potential applications in various fields. According to the 2023 McKinsey survey of different industries, marketing and sales, product and service development, and service operations are the most commonly reported uses of GenAl this year.

Popular Generative AI Tools

The table below shows examples of different Generative AI tools: chatbots, text-to-image generators, text-to-video generators, speech-to-text generators, and text-to-code generators. Some of them are already mature whereas others are still in beta testing (as marked on the table) but look promising.

GenAI typeApplicationsEngines/ModelsAccessDeveloper
ChatbotsChatGPTGPT-3.5, GPT-4Free, paidOpenAI
Bard BetaLaMDAFreeGoogle
Bing ChatGPT-4FreeMicrosoft
Text-to-image generatorsDALL-E 2 BetaGPT-3, CLIPFreeOpenAI
Midjourney BetaLLMPaidMidjourney
Stable DiffusionLDM, CLIPFreeStability AI
Text-to-video generatorsPika Labs BetaUnknownFreePika Labs
Gen-2LDMPaidRunaway
Imagen Video BetaCDM, U-NetN/AGoogle
Speech-to-text generatorsWhisperCustom GPTFreeOpenAI
Google Cloud Speech-to-TextConformer Speech Model technologyPaidGoogle
DeepgramCustom LLMPaidDeepgram
Text-to-code generatorsGitHub CopilotOpenAI CodexPaidGitHub, OpenAI
Amazon CodeWhispererUnknownFree, paidAmazon
ChatGPTGPT-3.5, GPT-4Free, paidOpenAI

These GenAI tools require specialized AI infrastructure, such as servers with IPU and GPU modules, to train and function. We will discuss IPUs and GPUs later. First, let’s understand how GenAI works on a higher level.

How Does Generative AI Work?

A GenAI system learns structures and patterns from a given dataset of similar content, such as massive amounts of text, photos, or music; for example, ChatGPT was trained on 570 GB of data from books, websites, research articles, and other forms of content available on the internet. According to ChatGPT itself, this is the equivalent of approximately 389,120 full-length eBooks in ePub format! Using that knowledge, the GenAI system then creates new and unique results. Here is a simplified illustration of this process:

Figure 1: A simplified process of how GenAI works

Let’s look at two key phases of how GenAI works: training GenAI on real data and generating new data.

Training on Real Data

To learn patterns and structures, GenAI systems utilize different types of machine learning and deep learning techniques, most commonly neural networks. A neural network is an algorithm that mimics the human brain to create a system of interconnected nodes that learn to process information by changing the weights of the connections between them. The most popular neural networks are GANs and VAEs.

Generative adversarial networks (GANs)

Generative adversarial networks (GANs) are a popular type of neural network used for GenAI training. Image generators DALL-E 2 and Midjourney were trained using GANs.

GANs operate by setting two neural networks against one another:

  • The generator produces new data based on the given real data set.
  • The discriminator determines whether the newly generated data is genuine or artificially generated, i.e., fake.

The generator tries to fool the discriminator. The ultimate goal is to generate data that the discriminator can’t distinguish from real data.

Variational autoencoders (VAEs)

Variational autoencoders (VAEs) are another well-known type of neural network used for image, text, music, and other content generation. The image generator Stable Diffusion was trained mostly using VAEs.

VAEs consist of two neural networks:

  • The encoder receives training data, such as a photo, and maps it to a latent space. Latent space is a lower dimensional representation of the data that captures the essential features of the input data.
  • The decoder analyzes the latent space and generates a new data sample, e.g., a photo imitation.

Comparing GANs and VAEs

Here are the basic differences between VAEs and GANs:

  • VAEs are probabilistic models, meaning they can generate new data that is more diverse than GANs.
  • VAEs are easier to train but don’t generally produce as high-quality images as GANs. GANs can be more difficult to work with but produce better photo-realistic images.
  • VAEs work better for signal processing use cases, such as anomaly detection for predictive maintenance or security analytics applications, while GANs are better at generating multimedia.

To get more efficient AI models, developers often train them using combinations of different neural networks.The entire training process can take minutes to months, depending on your goals, dataset, and resources.

Generating New Data

Once a generative AI tool has completed its training, it can generate new data; this stage is called inference. A user enters a prompt to generate the content, such as an image, a video, or a text. The GenAI system produces new data according to the user’s prompt.

For the most relevant results, it is ideal to train generative AI systems with a focus on a particular area. As a crude example, if you want a GenAI system to produce high-quality images of kangaroos, it’s best to train the system on images of kangaroos rather than on all existing animals. That’s why gathering relevant data to train AI models is one of the key challenges. This requires the tight collaboration of subject matter experts and data scientists.

How IPU and GPU Help to Develop Generative AI

There are two primary options when it comes to how you develop a generative AI system. You can utilize a prebuilt AI model and fine-tune it to your needs, or embark on the ambitious journey of training an AI model from the ground up. Regardless of your approach, access to AI infrastructure—IPU and GPU servers—is indispensable. There are two main reasons for this:

  • GPU and IPU architectures are adapted for AI workloads
  • GPU and IPU are available in the Cloud

Adapted Architecture

Intelligence Processing Units (IPUs) and graphics processing units (GPUs) are specialized hardware designed to accelerate the training and inference of AI models, including models for GenAI training. Their main advantage is that each IPU or GPU module has thousands of cores simultaneously processing data. This makes them ideal for parallel computing, essential in AI training.

As a result, GPUs are usually better deep learning accelerators than, for example, CPUs, which are suitable for sequential tasks but not parallel processing. While the server version of the CPU can have a maximum of 128 cores, a processor in the IPU, for example, has 1472 cores.

Here are the basic differences between GPUs and IPUs:

  • GPUs were initially designed for graphics processing, but their efficient parallel computation capabilities also make them well-suited for AI workloads. GPUs are the ideal choice for training and inference ML models. There are several AI-focused GPU hardware vendors on the market, but the clear leader is NVIDIA.
  • IPUs are a newer type of hardware designed specifically for AI workloads. They are even more efficient than GPUs at performing parallel computations. IPUs are ideal for training and deploying the most sophisticated AI applications, like large language models (LLMs.) Graphcore is the developer and sole vendor of IPUs, but there are some providers, like Gcore, that offer Graphcore IPUs in the cloud.

Availability in the Cloud

Typically, even enterprise-level AI developers don’t buy physical IPU/GPU servers because they are extremely expensive, costing up to $270,000. Instead, developers rent virtual and bare metal IPU/GPU instances from cloud providers on a per-minute or per-hour basis. This is also more convenient because AI training is an iterative process. When you need to run the next training iteration, you rent a server or virtual machine and pay only for the time you actually use it. The same applies to deploying a trained GenAI system for user access: You’ll need the parallel processing capabilities of IPUs/GPUs for better inference speed when generating new data, so you have to either buy or rent this infrastructure.

What’s Important When Choosing AI Infrastructure?

When choosing AI infrastructure, you should consider which type of AI accelerator better suits your needs in terms of performance and cost.

GPUs are usually an easier way to train models since there are a lot of prebuilt frameworks adapted for GPUs, including PyTorch, TensorFlow, and PaddlePaddle. NVIDIA also offers CUDA for its GPUs; this is a parallel computing software that works perfectly with programming languages widely used in AI development, like C and C++. As a result, GPUs are more suitable if you don’t have deep knowledge of AI training and fine-tuning, and want to get results faster using prebuilt AI models.

IPUs are better than GPUs for complex AI training tasks because they were designed specifically for that task, not for video rendering, for example, as GPUs were originally designed to do. However, due to its newness, IPUs support fewer prebuilt AI frameworks out-of-the-box than GPUs. When you are trying to perform a novel AI training task and therefore don’t have a prebuilt framework, you need to adapt an AI framework or AI model and even write code from scratch to run it. All of this requires technical expertise. However, Graphcore is actively developing SDKs and instructions to ease the use of their hardware.

Graphcore’s IPUs also support packing, a technique that significantly reduces the time required to pre-train, fine-tune, and infer from LLMs. Below is an example of how IPUs excel GPUs in inference for a language learning model based on the BERT architecture when using packing.

Figure 2: IPU outperforms GPU in inference for a BERT-flavored LLM when using packing

Cost-effectiveness is another important consideration when choosing an AI infrastructure. Look for benchmarks that compare AI accelerators in terms of performance per dollar/euro. This can help you to identify efficient choices by finding the right balance between price and compute power, and could save you a lot of money if you plan a long-term project.

Understanding the potential costs of renting AI infrastructure helps you to plan your budget correctly. Research the prices of cloud providers and calculate how much a specific server with a particular configuration will cost you per minute, hour, day, and so on. For more accurate calculations, you need to know the approximate time you’ll need to spend on training. This requires some mathematical effort, especially if you’re developing a GenAI model from scratch. To estimate the training time, you can count the number of operations needed or look at the GPU time.

Our Generative AI Projects

Gcore’s GenAI projects offer powerful examples of the fine-tuning approach to AI training, using IPU infrastructure.

English to Luxembourgish Translation Service

Gcore’s speech-to-text AI service translates English speech into Luxembourgish text on the go. The tool is based on the Whisper neural network and has been fine-tuned by our AI developers.

Figure 3: The UI of Gcore’s speech-to-text AI service

The project is an example of fine-tuning an existing speech-to-text GenAI model when it doesn’t support a specific language. The base version of Whisper didn’t support Luxembourgish, so our developers had to train the model to help Whisper learn this skill. A GenAI tool with any local or rare language not supported by existing LLMs could be created in the same way.

AI Image Generator

Al Image Generator is a generative AI tool free for all users registered to the Gcore Platform. It takes your text prompts and creates images of different styles. To develop the Image Generator, we used the prebuilt Openjourney GenAI model. We fine-tuned it using datasets for specific areas, such as gaming, to extend its capabilities and generate a wider range of images. Like our speech-to-text service, the Image Generator is powered by Gcore’s AI IPU infrastructure.

Figure 4: Image examples generated by Gcore’s AI Image Generator

The AI Image Generator is an example of how GenAI models like Openjourney can be customized to generate data with the style and context you need. The main problem with a pretrained model is that it is typically trained on large datasets and may lack accuracy when you need more specific results, like a highly specific stylization. If the prebuilt model doesn’t produce content that matches your expectations, you can collect a more relevant dataset and train your model to get more accurate results, which is what we did at Gcore. This approach can save significant time and resources, as it doesn’t require training the model from scratch.

Future Gcore AI Projects

Here’s what’s in the works for Gcore AI:

  • Custom AI model tuning will help to develop AI models for different purposes and projects. A customer can provide their dataset to train a model for their specific goal. For example, you’ll be able to generate graphics and illustrations according to the company’s guidelines, which can reduce the burden on designers.
  • AI models marketplace will provide ready-made AI models and frameworks in Gcore Cloud, similar to how our Cloud App Marketplace provides prebuilt cloud applications. Customers will be able to deploy these AI models on Virtual Instances or Bare Metal servers with GPU and IPU modules and either use these models as they are or fine-tune them for specific use cases.

Conclusion

IPUs and GPUs are fundamental to parallel processing, neural network training, and inference. This makes such infrastructure essential for generative AI development. However, GenAI developers need to have a clear understanding of their training goals. This will allow them to utilize the AI infrastructure properly, achieving maximum efficiency and best use of resources.

Try IPU for free

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With our confidential computing solutions, you retain full control over your training assets—no data is shared, exposed, or compromised.Deliver next-gen gaming with Gcore AIAI continues to revolutionize industries, and gaming is no exception. The deployment of artificial intelligence can help make games even more exciting for players, as well as enabling developers to work smarter when creating new games.At Gcore, AI is our core and gaming is our foundation. AI is seamlessly integrated into all our solutions with one goal in mind: to help grow your business. As AI continues to evolve rapidly, we're committed to staying at the cutting edge and changing with the future. Contact us today to discover how Everywhere Inference can enhance your gaming offerings.Get a customized consultation about AI gaming deployment

How gaming studios can use technology to safeguard players

Online gaming can be an enjoyable and rewarding pastime, providing a sense of community and even improving cognitive skills. During the pandemic, for example, online gaming was proven to boost many players’ mental health and provided a vital social outlet at a time of great isolation. However, despite the overall benefits of gaming, there are two factors that can seriously spoil the gaming experience for players: toxic behavior and cyber attacks.Both toxic behavior and cyberattacks can lead to players abandoning games in order to protect themselves. While it’s impossible to eradicate harmful behaviors completely, robust technology can swiftly detect and ban bullies as well as defend against targeted cyberattacks that can ruin the gaming experience.This article explores how gaming studios can leverage technology to detect toxic behavior, defend against cyber threats, and deliver a safer, more engaging experience for players.Moderating toxic behavior with AI-driven technologyToxic behavior—including harassment, abusive messages, and cheating—has long been a problem in the world of gaming. Toxic behavior not only affects players emotionally but can also damage a studio’s reputation, drive churn, and generate negative reviews.The online disinhibition effect leads some players to behave in ways they may not in real life. But even when it takes place in a virtual world, this negative behavior has real long-term detrimental effects on its targets.While you can’t control how players behave, you can control how quickly you respond.Gaming studios can implement technology that makes dealing with toxic incidents easier and makes gaming a safer environment for everyone. While in the past it may have taken days to verify a complaint about a player’s behavior, today, with AI-driven security and content moderation, toxic behavior can be detected in real time, and automated bans can be enforced. The tool can detect inappropriate images and content and includes speech recognition to detect derogatory or hateful language.In gaming, AI content moderation analyzes player interactions in real time to detect toxic behavior, harmful content, and policy violations. Machine learning models assess chat, voice, and in-game media against predefined rules, flagging or blocking inappropriate content. For example, let’s say a player is struggling with in-game harassment and cheating. With AI-powered moderation tools, chat logs and gameplay behavior are analyzed in real time, identifying toxic players for automated bans. This results in healthier in-game communities, improved player retention, and a more pleasant user experience.Stopping cybercriminals from ruining the gaming experienceAnother factor negatively impacting the gaming experience on a larger scale is cyberattacks. Our recent Radar Report showed that the gaming industry experienced the highest number of DDoS attacks in the last quarter of 2024. The sector is also vulnerable to bot abuse, API attacks, data theft, and account hijacking.Prolonged downtime damages a studio’s reputation—something hackers know all too well. As a result, gaming platforms are prime targets for ransomware, extortion, and data breaches. Cybercriminals target both servers and individual players’ personal information. This naturally leads to a drop in player engagement and widespread frustration.Luckily, security solutions can be put in place to protect gamers from this kind of intrusion:DDoS protection shields game servers from volumetric and targeted attacks, guaranteeing uptime even during high-profile launches. In the event of an attack, malicious traffic is mitigated in real-time, preventing zero downtime and guaranteeing seamless player experiences.WAAP secures game APIs and web services from bot abuse, credential stuffing, and data breaches. It protects against in-game fraud, exploits, and API vulnerabilities.Edge security solutions reduce latency, protecting players without affecting game performance. The Gcore security stack helps ensure fair play, protecting revenue and player retention.Take the first steps to protecting your customersGaming should be a positive and fun experience, not fraught with harassment, bullying, and the threat of cybercrime. Harmful and disruptive behaviors can make it feel unsafe for everyone to play as they wish. That’s why gaming studios should consider how to implement the right technology to help players feel protected.Gcore was founded in 2014 with a focus on the gaming industry. Over the years, we have thwarted many large DDoS attacks and continue to offer robust protection for companies such as Nitrado, Saber, and Wargaming. Our gaming specialization has also led us to develop game-specific countermeasures. If you’d like to learn more about how our cybersecurity solutions for gaming can help you, get in touch.Speak to our gaming solutions experts today

Gcore and Northern Data Group partner to transform global AI deployment

Gcore and Northern Data Group have joined forces to launch a new chapter in enterprise AI. By combining high-performance infrastructure with intelligent software, the commercial and technology partnership will make it dramatically easier to deploy AI applications at scale—wherever your users are. At the heart of this exciting new partnership is a shared vision: global, low-latency, secure AI infrastructure that’s simple to use and ready for production.Introducing the Intelligence Delivery NetworkAI adoption is accelerating, but infrastructure remains a major bottleneck. Many enterprises discover blockers regarding latency, compliance, and scale, especially when deploying models in multiple regions. The traditional cloud approach often introduces complexity and overhead just when speed and simplicity matter most.That’s where the Intelligence Delivery Network (IDN) comes in.The IDN is a globally distributed AI network built to simplify inference at the edge. It combines Northern Data’s state-of-the-art infrastructure with Gcore Everywhere Inference to deliver scalable, high-performance AI across 180 global points of presence.By locating AI workloads closer to end users, the IDN reduces latency and improves responsiveness—without compromising on security or compliance. Its geo-zoned, geo-balanced architecture ensures resilience and data locality while minimizing deployment complexity.A full AI deployment toolkitThe IDN is a full AI deployment toolkit built on Gcore’s cloud-native platform. The solution offers a vertically integrated stack designed for speed, flexibility, and scale. Key components include the following:Managed Kubernetes for orchestrationA container-based deployment engine (Docker)An extensive model library, supporting open-source and custom modelsEverywhere Inference, Gcore’s software for distributing inferencing across global edge points of presenceThis toolset enables fast, simple deployments of AI workloads—with built-in scaling, resource management, and observability. The partnership also unlocks access to one of the world’s largest liquid-cooled GPU clusters, giving AI teams the horsepower they need for demanding workloads.Whether you’re building a new AI-powered product or scaling an existing model, the IDN provides a clear path from development to production.Built for scale and performanceThe joint solution is built with the needs of enterprise customers in mind. It supports multi-tenant deployments, integrates with existing cloud-native tools, and prioritizes performance without sacrificing control. Customers gain the flexibility to deploy wherever and however they need, with enterprise-grade security and compliance baked in.Andre Reitenbach, CEO of Gcore, comments, “This collaboration supports Gcore’s mission to connect the world to AI anywhere and anytime. Together, we’re enabling the next generation of AI applications with low latency and massive scale.”“We are combining Northern Data’s heritage of HPC and Data Center infrastructure expertise, with Gcore’s specialization in software innovation and engineering.” says Aroosh Thillainathan, Founder and CEO of Northern Data Group. “This allows us to accelerate our vision of delivering software-enabled AI infrastructure across a globally distributed compute network. This is a key moment in time where the use of AI solutions is evolving, and we believe that this partnership will form a key part of it.”Deploy AI smarter and faster with Gcore and Northern Data GroupAI is the new foundation of digital business. Deploying it globally shouldn’t require a team of infrastructure engineers. With Gcore and Northern Data Group, enterprise teams get the tools and support they need to run AI at the edge at scale and at speed.No matter what you and your teams are trying to achieve with AI, the new Intelligence Delivery Network is built to help you deploy smarter and faster.Read the full press release

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