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NVIDIA H100 Hopper PCIe 80GB Graphics Card, 80GB HBM2e, 5120-Bit, PCIe 5.0, Best FIT for Data Center and Deep Learning
Equipped with 80 GB of HBM2e memory and a broad 5120-bit memory bus, the H100 PCIe 80 GB achieves a remarkable bandwidth of 2,039 GB/s, ensuring smooth and responsive performance for professional workloads.
NVIDIA H100 Hopper PCIe 80GB Graphics Card, 80GB HBM2e, 5120-Bit, PCIe 5.0, Best FIT for Data Center and Deep Learning
Item #: 83917520

NVIDIA H100 Hopper PCIe 80GB Graphics Card, 80GB HBM2e, 5120-Bit, PCIe 5.0, Best FIT for Data Center and Deep Learning

Item #: 83917520

QAR 125279

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Equipped with 80 GB of HBM2e memory and a broad 5120-bit memory bus, the H100 PCIe 80 GB achieves a remarkable bandwidth of 2,039 GB/s, ensuring smooth and responsive performance for professional workloads.
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What Stands Out

Massive Memory
With 80GB HBM2e, the NVIDIA H100 offers unrivaled memory capacity, ensuring efficient handling of large datasets and complex models for deep learning applications in data centers.
High Bandwidth
The 5120-Bit memory interface provides exceptional bandwidth, enhancing data throughput and processing speed, which is crucial for accelerating AI and deep learning tasks.
Advanced Connectivity
Utilizing PCIe 5.0 technology, the H100 enables rapid data transfer rates, ensuring compatibility with modern systems while improving overall performance in demanding environments.

Product Details

Shop NVIDIA H100 Hopper PCIe 80GB Graphics Card, 80GB HBM2e, 5120-Bit, PCIe 5.0, Best FIT for Data Center and Deep Learning online at a best price in Qatar. B0CXBNNNSD
Country of OriginThis item will be imported from US
Item Weight4.5 lbs (2.04 kg)
What is in the boxNVIDIA H100 Hopper PCIe... For more details, please check description/product details

Who Should Buy?

Suitable For
  • Data Center Managers

    Perfect for professionals managing data center workloads, requiring powerful performance for virtualization and high-demand applications.

  • AI Researchers

    Ideal for AI and machine learning researchers needing high memory bandwidth and performance for complex model training.

  • Deep Learning Engineers

    A fit for engineers developing deep learning solutions, requiring advanced computational capabilities and large memory for datasets.

Not Suitable For
  • Casual Gamers

    Not suitable for casual gamers due to high cost and advanced features not necessary for regular gaming.

  • Budget-Conscious Users

    Cost-prohibitive for users on a budget; alternatives offer adequate performance for less intensive computing needs.

  • Home Office Users

    Overkill for home office setups; standard graphics cards suffice for everyday productivity tasks and light media consumption.

Product Description

NVIDIA H100 Hopper PCIe 80GB Graphics Card, 80GB HBM2e, 5120-Bit, PCIe 5.0, Best FIT for Data Center and Deep Learning

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Customer Questions & Answers

  • Question: What is the NVIDIA H100 Hopper PCIe 80GB Graphics Card best used for?

    Answer: The NVIDIA H100 Hopper PCIe 80GB Graphics Card is optimized for data centers and deep learning applications. With its powerful 80GB HBM2e memory and 5120-bit memory bandwidth, it excels in handling large datasets and complex computational tasks. This makes it ideal for training AI models and performing massive data analytics, ensuring faster processing times compared to other GPUs. Organizations leveraging deep learning for AI research, natural language processing, or computer vision can significantly benefit from this technology.
  • Question: What are the benefits of using PCIe 5.0 with the NVIDIA H100?

    Answer: PCIe 5.0 technology enhances data transfer speeds up to 32 GT/s, which is twice as fast as its predecessor. This high-speed interface allows the NVIDIA H100 Hopper to handle more data concurrently, reducing bottlenecks in data flow between the GPU and other components. In scenarios where heavy data workloads such as machine learning or real-time analytics are common, PCIe 5.0 optimizes performance and efficiency, making it a valuable asset for any modern data center.
  • Question: How does the 80GB HBM2e memory affect performance?

    Answer: The 80GB HBM2e memory in the NVIDIA H100 Hopper enables high bandwidth and capacity for memory-intensive tasks. This allows for faster data access and improves overall computational performance, especially for deep learning algorithms that require large datasets to be processed in parallel. For example, when training deep neural networks or conducting simulations in research, the ample memory capacity helps to reduce time constraints and facilitates more complex models with less latency.
  • Question: Can the NVIDIA H100 be used for gaming?

    Answer: While the NVIDIA H100 Hopper can technically run games, it is primarily designed for enterprise-level applications, particularly in AI and data processing, rather than gaming. Its architecture and capabilities are tailored to support demanding computations found in data centers and deep learning tasks rather than providing the graphics performance that gaming requires. Those interested in high-performance gaming should consider NVIDIA's dedicated gaming GPUs instead.
  • Question: What industries benefit from the NVIDIA H100 Hopper?

    Answer: The NVIDIA H100 Hopper is particularly beneficial for industries such as healthcare, autonomous vehicles, finance, and research. In healthcare, for instance, it can accelerate medical imaging and diagnostics, while in finance, it can analyze real-time trading data. The graphics card’s deep learning capabilities make it ideal for enhancing AI-driven applications and processes, leading to improved efficiency and innovation in these fields.
  • Question: Is the NVIDIA H100 suitable for cloud computing environments?

    Answer: Yes, the NVIDIA H100 Hopper is an excellent choice for cloud computing environments. Its robust architecture is designed to support multiple users and workloads, making it ideal for cloud service providers offering GPU-as-a-service. This benefits businesses looking to leverage scalable computing power for AI, machine learning, or big data processing, ensuring that they can handle varying workloads efficiently in cloud-based infrastructure.
  • Question: How does the NVIDIA H100 improve AI model training times?

    Answer: The NVIDIA H100 Hopper significantly reduces training times for AI models due to its high-speed memory and advanced processing capabilities. The card’s architecture allows complex computations to be executed more rapidly, which is crucial for tasks like deep learning model training that generate vast amounts of data. Thus, researchers and developers can iterate more quickly, leading to faster implementation and discovery cycles in their AI projects.
  • Question: What makes the NVIDIA H100 different from its predecessors?

    Answer: The NVIDIA H100 Hopper distinguishes itself from its predecessors through its advanced architecture and higher memory capacity. With features like PCIe 5.0 support and HBM2e memory, it offers superior data handling and transfer speed, which enhances performance for high computational loads. This makes it particularly suitable for tackling the challenges posed by modern AI workloads that older models may not handle as efficiently.
  • Question: Can the NVIDIA H100 be integrated with existing data center infrastructure?

    Answer: Yes, the NVIDIA H100 Hopper is designed to integrate seamlessly with existing data center infrastructure. Its PCIe 5.0 interface is compatible with most modern servers, facilitating straightforward installation and setup. Organizations can enhance their computational capabilities by adding the H100 to their infrastructure, allowing for increased processing power without the need for significant overhauls to their existing systems.
  • Question: Where can I buy the NVIDIA H100 Hopper PCIe 80GB Graphics Card?

    Answer: You can purchase the NVIDIA H100 Hopper PCIe 80GB Graphics Card from Ubuy in Qatar. Ubuy offers a reliable platform for acquiring high-performance computing hardware, ensuring you have access to the latest technology essential for data centers and deep learning applications.

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Pros

  • High memory capacity
  • Fast processing speed
  • Efficient power use

Cons

  • High cost

Product Price History

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