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Maximizing AI and HPC Workloads with NVIDIA H200 Tensor Core GPU

  • By Gcore
  • January 9, 2024
  • 6 min read
Maximizing AI and HPC Workloads with NVIDIA H200 Tensor Core GPU

In the past decade, the fields of artificial intelligence (AI) in software and high-performance computing (HPC) in hardware have undergone a significant transformation. This revolution has led to breakthrough applications across diverse sectors, improving human life through advancements in fields such as genomic sequencing, fuel conservation, and earthquake prediction. Developing such applications requires researchers and developers to work with a gigantic volume of data using complex algorithms. Such demanding computing tasks require a new generation of data processors capable of tackling parallel and sophisticated workloads. In this article, we’ll explain how the upcoming NVIDIA H200 Tensor Core GPU, slated for release in Q2 of 2024, will help to optimize your AI and HPC workloads. We will also explore how this technology enables the efficient creation of data-driven applications and discuss the current alternatives available.

Why Use GPU for AI and HPC Workloads?

Let’s start by exploring why GPUs are the hardware of choice for running AI and HPC workloads, and what kinds of workloads we’re talking about.

Benefits of GPUs Compared to CPUs

To understand why the new NVIDIA H200 GPU (graphics processing unit) is so innovative compared to its previous GPU, it’s important to understand how GPUs differ from CPUs (central processing units) in application development. GPUs offer distinct advantages in processing speed and efficiency, particularly for tasks involving parallel calculations on a massive amount of data. For an in-depth look at the differences between GPUs and CPUs, particularly in the context of deep learning, see our detailed article on deep learning GPUs.

What Are AI Workloads and Where Do GPUs Fit In?

The development of AI applications encompasses a series of critical processes, each contributing to what we term “AI workloads.” These processes include:

  • Data collection
  • Data preprocessing
  • Model selection
  • Model training
  • Model testing
  • Model optimization
  • Deployment
  • Continuous learning

GPUs play a major role in accelerating these workloads, especially in tasks like deep learning, due to their ability to process thousands of threads simultaneously, making them excellent at handling the complex mathematical computations required in AI development. The H200 stands out as an apt choice for such tasks due to its impressive computational power and high memory bandwidth, which significantly reduce the time taken to train and run AI models, thereby boosting efficiency and productivity.

Industry Use Cases for NVIDIA H200

Recent significant advances in high-performance computing and breakthroughs in large language models have expanded research and application development into previously challenging fields like climate science and genome sequencing. The NVIDIA H200, anticipated to be the highest-performance GPU on the market, will play a crucial role in this progress when it’s released in Q2 2024. To explore the advancements of the NVIDIA H200 over its predecessors like the A100 and H100, check out our comparative analysis.

Scientific Simulations

NVIDIA has a history of facilitating rapid scientific simulations in areas such as 3D workflows, and catalyst modeling, particularly through products like the NVIDIA Omniverse. This facilitates the development of 3D workflows and applications based on Universal Scene Description (USD) across various industries, including robotics, gaming, chemicals, and automotive. A notable example is the BMW Group, which used NVIDIA Omniverse to create its first entirely virtual factory.

The introduction of the H200 chip is expected to further accelerate and enhance these scientific simulations within the NVIDIA product range.

Genomic Software

In biomedical engineering, a field poised for rapid growth, NVIDIA Clara is a key player. This genomic software suite accelerates various genome sequencing analyses, supporting the development of a wide range of healthcare applications, including drug discovery, precision medicine, and clinical diagnostics.

With the introduction of the H200 chip, genomic software could yield faster, more affordable results. This could positively affect patient outcomes in areas like cancer treatment.

Financial Analysis

In the past, financial analysis used CPUs as the main calculation engine. With the evolution of AI and use of GPUs, faster and more precise financial analysis calculations are possible. Many cloud providers now offer AI GPU infrastructure, enabling financial firms to adopt a cost-effective, pay-as-you-go approach to their research and application development.

The new H200 chip further accelerates research outcomes and the development of new features, providing a competitive edge in the fast-paced and risk-oriented financial market.

Image and Video Processing

Industries like gaming and streaming require enormous computing power and memory for image and video processing. With this in mind, NVIDIA created the NVIDIA RTX technology which has revolutionized computer graphics with the latest enhancements in AI and simulations.

Wylie Co., a digital imagery studio known for its Oscar-winning work on Dune, is one of the most successful studios to have heavily applied AI successfully in its rendering process, wire removal, and visual effects. After switching from CPUs to GPUs, Wylie achieved a 24-fold performance improvement and reduced energy consumption to one-fifth of its previous level.

With the introduction of the new H200 chip, companies working with image and video processing can raise their performance while simultaneously reducing energy consumption—a win for business revenue and for the environment.

How NVIDIA H200 Drives AI and HPC Workloads

The NVIDIA H200 boasts innovative HBM3e memory technology, delivering 141 GB of memory at a remarkable speed of 4.8 TB per second. This doubles the capacity and delivers a 2.4-fold increase in bandwidth compared to the NVIDIA A100 chip. The H200 GPU chip also offers 1.4 times more bandwidth than the H100 GPU, marking a significant leap in performance and energy efficiency. Detailed specifications can be found on the NVIDIA H200 data sheet.

High-Performance AI Inference

The NVIDIA H200 will drive a new era in high-performance AI inference. For those new to the concept, AI inference is the process where a trained machine learning model applies its knowledge to previously unseen data, generating relevant outputs for specific inputs in a given context. For a comprehensive understanding of how it works, take a look at our detailed AI inference article. This process is integral to the functionality of large language models (LLMs) like OpenAI’s Chat-GPT or Google’s Bard, which have seen dramatic growth over the last few years. Notable large language models include GPT-3 175B, Llama2 70B, and Llama2 13B.

As LLMs advance, the demand for more powerful hardware—such as the H200—to support their inference processes increases. The NVIDIA H200 GPU has demonstrated exceptional performance, offering 18 times the efficiency of the A100 GPU in a recent benchmark for the GPT-3 175B model. The NVIDIA H100 GPU, by comparison, proved to be 11 times more efficient than the A100 GPU.

Figure 1: Performance improvement for GPT-3 application using A100, H100, and H200 GPUs

The NVIDIA GPU chip comes with the latest version of its own built-in LLM, TensorRT-LLM, a toolkit that provides optimized solutions for inferencing large language models like GPT-3 and Llama 2. When running on the Llama-70B model, the new NVIDIA H200 chip achieves a 1.9-fold improvement in throughput optimization over the NVIDIA H100, which uses the previous version of TensorRT-LLM.

Figure 2: Throughput improvement that the H200 GPU chip offers when running on the Llama 70B model, compared with the previous H100 chip

In the case of the Llama 2 13B model, the NVIDIA H200 GPU chip achieves up to 1.4 times the throughput optimization compared to its predecessor, the NVIDIA H100 GPU.

Figure 3: Details of the TensorRT-LLM improvement for the H200 chip, compared to the H100 chip

Stability AI, one of the world’s leading open-source generative AI companies, significantly enhanced the speed of its text-to-image AI product by integrating NVIDIA TensorRT. This integration, along with the use of the converted ONNX (Open Neural Network Exchange) model on NVIDIA H100 chips led to a notable performance gain. This product can now generate high-definition images in just 1.47 seconds, effectively doubling its previous performance.

Below is a summary of the enhanced image throughput performance achieved by Stable Diffusion XL 1.0. This model, designed to generate and modify images based on text prompts, was created by Stability AI in collaboration with NVIDIA.

Figure 4: Details in improvement for image throughput for 30 steps at 1024×1024 for Stable Diffusion XL 1.0

By applying the NVIDIA TensorRT library to their chips, Stability AI has significantly enhanced the image throughput of Stable Diffusion XL 1.0, particularly on the NVIDIA H100 chips, achieving up to a 70% improvement.

With the introduction of the more optimized latest TensorRT version on the NVIDIA H200 chip, companies like Stability AI, utilizing open-source, generative libraries such as Stable Diffusion XL 1.0, are poised to experience even greater performance gains.

High-Performance Computing

In AI, handling complex calculations on large datasets requires robust high-performance computing (HPC) capabilities. The NVIDIA H200 chip significantly improves performance, showing a 110-fold improvement over the Dual x86 CPU in the MILC project, a collaborative project studying the theory of strong interactions of subatomic physics.

Figure 5: H200 performance improvement for the MILC project, compared to the Dual x86 CPU

On average, the NVIDIA H200 chip doubles the performance of the NVIDIA A100 chip for HPC applications, compared to the 1.7-fold improvement shown by the NVIDIA H100.

Figure 6: Overall performance improvement of the H200 for HPC applications compared to the H100 and A100 GPUs

Energy Efficiency

In recent years, software companies have grown aware of the environmental impact of software development. NVIDIA employs sustainable computing best practices when creating its products.

With the H200 GPU chip, energy efficiency and TCO (total cost of ownership)—an estimation of the expenses associated with purchasing, deploying, using, and retiring a piece of equipment—reach new levels. While the H200 GPU chip offers enormous performance improvements, it only consumes as much power as its predecessor, the H100. Moreover, the H200 GPU is 50% more efficient than the H100 for both energy usage and total cost of ownership.

Figure 7: The H200 chip’s reduction in LLM energy use compared to the H100

This significant improvement in energy efficiency for the H200 GPU chip is achieved by optimizing the NVIDIA Hopper architecture, enabling better performance from its GPUs.

Conclusion

The new NVIDIA H200 Tensor Core GPU helps to maximize the AI and HPC workloads so that you can create data-intensive applications more easily and effectively. However, NVIDIA H200 GPU chips are expected to be costly to run, especially for projects that are in MVP phases or applications that only occasionally need to work with large volumes of data and workloads. In such scenarios, it’s prudent to consider a cloud platform that offers a pay-per-use plan, rather than investing in high-performance GPU infrastructure yourself.

With Gcore AI GPU infrastructure, you can choose the NVIDIA GPU solution that best fits your workload. You’re only charged for actual use, optimizing the cost of your AI infrastructure. We offer the latest and most efficient options, including A100s and H100s, with L40S coming soon. Explore the differences in our dedicated article.

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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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