• FEATURED STORY OF THE WEEK

      NVIDIA H200 vs Gaudi 3: The AI GPU Battle Heats Up

      Written by :  
      uvation
      Team Uvation
      11 minute read
      August 1, 2025
      Industry : energy-utilities
      NVIDIA H200 vs Gaudi 3: The AI GPU Battle Heats Up
      Bookmark me
      Share on
      Reen Singh
      Reen Singh

      Writing About AI

      Uvation

      Reen Singh is an engineer and a technologist with a diverse background spanning software, hardware, aerospace, defense, and cybersecurity. As CTO at Uvation, he leverages his extensive experience to lead the company’s technological innovation and development.

      Explore Nvidia’s GPUs

      Find a perfect GPU for your company etc etc
      Go to Shop

      FAQs

      • The NVIDIA H200 is an upgrade of NVIDIA’s Hopper architecture, featuring a substantial 141 GB of HBM3e memory with a bandwidth of 4.8 TB/s. It is manufactured using TSMC’s 4nm process and has a high Thermal Design Power (TDP) of 700W. In contrast, the Intel Gaudi 3 uses a custom architecture, including 96 MB of on-chip SRAM and 128 GB of HBM2e memory, providing 3.7 TB/s bandwidth. It is built on a 5nm TSMC process and has a lower TDP of 600W. The H200 prioritises memory bandwidth, whereas the Gaudi 3 focuses on integrated SRAM and software optimisations for efficient AI workload processing.

      • For training large AI models like Llama 70B, the NVIDIA H200 excels due to its superior HBM3e memory bandwidth, which allows for faster data processing and reduces bottlenecks. The Intel Gaudi 3 also offers strong training performance, with Intel claiming it trains Llama 70B models 1.7 times faster than the NVIDIA H100 (H200’s predecessor), partly by using FP8 precision.

         

        In terms of inference, the NVIDIA H200 is strong in memory-bound tasks requiring large data batches due to its higher memory bandwidth. The Intel Gaudi 3, with its eight dedicated Matrix Math Engines, is optimised for complex matrix multiplications central to transformer models, leading to claims of being 1.3 times faster than the H200 in certain inference tasks. The overall performance depends on the specific AI task and model architecture.

      • The NVIDIA H200 has a higher TDP of 700W, demanding advanced and potentially more costly cooling solutions. Its focus is on maximum raw performance, even at the expense of higher energy consumption per operation. The Intel Gaudi 3 operates at a lower 600W TDP, making it easier and cheaper to cool, often allowing for standard air cooling. The Gaudi 3 prioritises performance-per-watt, aiming to achieve more AI tasks per kilowatt-hour of electricity, making it appealing for cost-conscious or eco-focused deployments. For scalability in large clusters, the Gaudi 3’s lower TDP allows for denser packing of accelerators in server racks without exceeding power or cooling limits.

      • NVIDIA holds a significant advantage with its mature CUDA ecosystem, a programming platform deeply integrated with major AI frameworks like PyTorch and TensorFlow. CUDA’s extensive documentation, polished tools like TensorRT, and a large developer community drastically reduce development time and risk for existing AI projects.

         

        Intel Gaudi 3 relies on its Habana SynapseAI software suite, which supports popular frameworks but is less mature than CUDA. A major challenge for Gaudi 3 is the effort required to migrate existing AI code written for NVIDIA GPUs, as SynapseAI does not directly run CUDA code. While this presents a learning curve and potential delays, Intel’s aggressive pricing strategy aims to offset this, offering significant hardware cost savings for organisations willing to adapt their code.

      • The NVIDIA H200 is positioned as a premium product with an estimated starting price well above $40,000 per unit, similar to its predecessor. It has begun shipping in limited quantities, but supply is constrained, potentially leading to delays.

         

        In contrast, the Intel Gaudi 3 is expected to be significantly cheaper, with industry estimates suggesting it could cost 30% to 40% less than the H100. Volume availability for the Gaudi 3 is anticipated in the second half of 2025, with Intel partnering with major server builders to broaden its reach.

      • The Intel Gaudi 3 offers a more attractive Total Cost of Ownership (TCO) due to its significantly lower estimated purchase price and slightly reduced power draw (600W vs 700W). This makes it highly appealing for budget-sensitive or large-scale deployments where numerous GPU units are required, especially for workloads where its performance is competitive.

         

        The NVIDIA H200, despite its higher upfront cost, delivers unmatched performance for memory-intensive tasks and training massive AI models. For projects where absolute speed and the ability to handle huge datasets are paramount, the H200’s premium can be justified, offering superior capability per GPU in these specific scenarios.

      • The NVIDIA H200 is the top choice for training the largest language models and handling memory-intensive research tasks, particularly where achieving the fastest possible training times for frontier AI models is critical and budget is a secondary concern. Its 141 GB HBM3e memory and 4.8 TB/s bandwidth make it ideal for such demands.

         

        The Intel Gaudi 3 is better suited for organisations building large-scale inference clusters or those needing to balance performance with tight budgets. Its lower cost and competitive performance in key workloads like BERT, combined with efficient inference capabilities, make its price-to-performance ratio highly attractive for practical deployments.

      • The intense competition between NVIDIA and Intel signifies a significant heating up of the AI accelerator battle. Intel is aggressively challenging NVIDIA’s long-standing dominance by offering the Gaudi 3 as a compelling value proposition against NVIDIA’s higher-priced H200. Meanwhile, NVIDIA continues to push the boundaries of memory technology and peak performance with innovations like HBM3e. This rivalry is expected to lead to the development of more powerful and accessible options for AI developers in the coming years, fostering innovation and potentially driving down costs across the industry.

      More Similar Insights and Thought leadership

      NVIDIA DGX BasePOD™: Accelerating Enterprise AI with Scalable Infrastructure

      NVIDIA DGX BasePOD™: Accelerating Enterprise AI with Scalable Infrastructure

      The NVIDIA DGX BasePOD™ is a pre-tested, ready-to-deploy blueprint for enterprise AI infrastructure, designed to solve the complexity and time-consuming challenges of building AI solutions. It integrates cutting-edge components like the NVIDIA H200 GPU and optimises compute, networking, storage, and software layers for seamless performance. This unified, scalable system drastically reduces setup time from months to weeks, eliminates compatibility risks, and maximises resource usage. The BasePOD™ supports demanding AI workloads like large language models and generative AI, enabling enterprises to deploy AI faster and scale efficiently from a few to thousands of GPUs.

      11 minute read

      Energy and Utilities

      Data Sovereignty vs Data Residency vs Data Localization in the AI Era

      Data Sovereignty vs Data Residency vs Data Localization in the AI Era

      In the AI era, data sovereignty (legal control based on location), residency (physical storage choice), and localization (legal requirement to keep data local) are critical yet complex concepts. Their interplay significantly impacts AI development, requiring massive datasets to comply with diverse global laws. Regulations like GDPR, China’s PIPL, and Russia’s Federal Law No. 242-FZ highlight these challenges, with rulings such as Schrems II demonstrating that legal agreements cannot always override conflicting national laws where data is physically located. This leads to fragmented compliance, increased costs, and potential AI bias due to limited data inputs. Businesses can navigate this by leveraging federated learning, synthetic data, sovereign clouds, and adaptive infrastructure. Ultimately, mastering these intertwined challenges is essential for responsible AI, avoiding penalties, and fostering global trust.

      11 minute read

      Energy and Utilities

      NVIDIA DGX H200 vs. DGX B200: Choosing the Right AI Server

      NVIDIA DGX H200 vs. DGX B200: Choosing the Right AI Server

      Artificial intelligence is transforming industries, but its complex models demand specialized computing power. Standard servers often struggle. That’s where NVIDIA DGX systems come in – they are pre-built, supercomputing platforms designed from the ground up specifically for the intense demands of enterprise AI. Think of them as factory-tuned engines built solely for accelerating AI development and deployment.

      16 minute read

      Energy and Utilities

      H200 Computing: Powering the Next Frontier in Scientific Research

      H200 Computing: Powering the Next Frontier in Scientific Research

      The NVIDIA H200 GPU marks a groundbreaking leap in high-performance computing (HPC), designed to accelerate scientific breakthroughs. It addresses critical bottlenecks with its unprecedented 141GB of HBM3e memory and 4.8 TB/s memory bandwidth, enabling larger datasets and higher-resolution models. The H200 also delivers 2x faster AI training and simulation speeds, significantly reducing experiment times. This powerful GPU transforms fields such as climate science, drug discovery, genomics, and astrophysics by handling massive data and complex calculations more efficiently. It integrates seamlessly into modern HPC environments, being compatible with H100 systems, and is accessible through major cloud platforms, making advanced supercomputing more democratic and energy-efficient

      9 minute read

      Energy and Utilities

      AI Inference Chips Latest Rankings: Who Leads the Race?

      AI Inference Chips Latest Rankings: Who Leads the Race?

      AI inference is happening everywhere, and it’s growing fast. Think of AI inference as the moment when a trained AI model makes a prediction or decision. For example, when a chatbot answers your question or a self-driving car spots a pedestrian. This explosion in real-time AI applications is creating huge demand for specialized chips. These chips must deliver three key things: blazing speed to handle requests instantly, energy efficiency to save power and costs, and affordability to scale widely.

      13 minute read

      Energy and Utilities

      Beyond Sticker Price: How NVIDIA H200 Servers Slash Long-Term TCO

      Beyond Sticker Price: How NVIDIA H200 Servers Slash Long-Term TCO

      While NVIDIA H200 servers carry a higher upfront price, they deliver significant long-term savings that dramatically reduce Total Cost of Ownership (TCO). This blog breaks down how H200’s efficiency slashes operational expenses—power, cooling, space, downtime, and staff productivity—by up to 46% compared to older GPUs like the H100. Each H200 server consumes less energy, delivers 1.9x higher performance, and reduces data center footprint, enabling fewer servers to do more. Faster model training and greater reliability minimize costly downtime and free up valuable engineering time. The blog also explores how NVIDIA’s software ecosystem—CUDA, cuDNN, TensorRT, and AI Enterprise—boosts GPU utilization and accelerates deployment cycles. In real-world comparisons, a 100-GPU H200 cluster saves over $6.7 million across five years versus an H100 setup, reaching a payback point by Year 2. The message is clear: the H200 isn’t a cost—it’s an investment in efficiency, scalability, and future-proof AI infrastructure.

      9 minute read

      Energy and Utilities

      NVIDIA H200 vs H100: Better Performance Without the Power Spike

      NVIDIA H200 vs H100: Better Performance Without the Power Spike

      Imagine training an AI that spots tumors or predicts hurricanes—cutting-edge science with a side of electric shock on your utility bill. AI is hungry. Really hungry. And as models balloon and data swells, power consumption is spiking to nation-sized levels. Left unchecked, that power curve could torch budgets and bulldoze sustainability targets.

      5 minute read

      Energy and Utilities

      Improving B2B Sales with Emerging Data Technologies and Digital Tools

      Improving B2B Sales with Emerging Data Technologies and Digital Tools

      The B2B sales process is always evolving. The advent of Big Data presents new opportunities for B2B sales teams as they look to transition from labor-intensive manual processes to a more informed, automated approach.

      7 minute read

      Energy and Utilities

      The metaverse is coming, and it’s going to change everything.

      The metaverse is coming, and it’s going to change everything.

      The metaverse is coming, and it's going to change everything. “The metaverse... lies at the intersection of human physical interaction and what could be done with digital innovation,” says Paul von Autenried, CIO at Bristol-Meyers Squibb Co. in the Wall Street Journal.

      9 minute read

      Energy and Utilities

      What to Expect from Industrial Applications of Humanoid Robotics

      What to Expect from Industrial Applications of Humanoid Robotics

      obotics engineers are designing and manufacturing more robots that resemble and behave like humans—with a growing number of real-world applications. For example, humanoid service robots (SRs) were critical to continued healthcare and other services during the COVID-19 pandemic, when safety and social distancing requirements made human services less viable,

      7 minute read

      Energy and Utilities

      How the U.S. Military is Using 5G to Transform its Networked Infrastructure

      How the U.S. Military is Using 5G to Transform its Networked Infrastructure

      Across the globe, “5G” is among the most widely discussed emerging communications technologies. But while 5G stands to impact all industries, consumers are yet to realize its full benefits due to outdated infrastructure and a lack of successful real-world cases

      5 minute read

      Energy and Utilities

      The Benefits of Managed Services

      The Benefits of Managed Services

      It’s more challenging than ever to find viable IT talent. Managed services help organzations get the talent they need, right when they need it. If you’re considering outsourcing or augmenting your IT function, here’s what you need to know about the benefits of partnering with a managed service provider. Managed services can provide you with strategic IT capabilities that support your long-term goals. Here are some of the benefits of working with an MSP.

      5 minute read

      Energy and Utilities

      These Are the Most Essential Remote Work Tools

      These Are the Most Essential Remote Work Tools

      It all started with the global pandemic that startled the world in 2020. One and a half years later, remote working has become the new normal in several industries. According to a study conducted by Forbes, 74% of professionals expect remote work to become a standard now.

      7 minute read

      Energy and Utilities

      uvation