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    NVIDIA Blackwell: Scaling Exascale AI and Trillion Parameter Models

    22 min
    |
    |
    11 mag 2026
    AITechnologyScience

    Explore how NVIDIA Blackwell scales exascale AI and trillion parameter models. Learn about AI factories, overcoming the bandwidth wall, and the future of AI reasoning.

    NVIDIA Blackwell: Scaling Exascale AI and Trillion Parameter Models

    Miglior citazione da NVIDIA Blackwell: Scaling Exascale AI and Trillion Parameter Models

    “

    We’ve moved from computers as tools we use to computers as infrastructure that produces intelligence. The Blackwell architecture is the first one designed from the ground up to be the engine for that AI factory.

    ”

    Questa lezione audio è stata creata da un membro della comunità BeFreed

    Domanda di input

    This lesson is part of the learning plan: 'NVIDIA Full-Stack Technology Guide'. Lesson topic: NVIDIA Blackwell: Scaling Exascale AI Overview: Training multi-trillion parameter models requires massive bandwidth. Learn how NVLink and secure computing enable high-performance AI scaling. Key insights to cover in order: 1. Fifth-generation NVLink scales up to 576 GPUs to provide the bandwidth necessary for multi-trillion parameter models. 2. The NVLink Switch Chip delivers 130TB/s of bandwidth in a single 72-GPU domain for massive efficiency gains. 3. Confidential Computing provides hardware-based security that protects sensitive AI models without sacrificing throughput performance during training. Listener profile: - Learning goal: 了解英伟达技术产品介绍 - Background knowledge: 我接触过英伟达的GeForce显卡、RTX系列、CUDA编程、Tegra芯片和自动驾驶平台。 - Guidance: 重点介绍英伟达各产品线的技术特点和应用场景,包括游戏显卡、专业计算、移动处理器和自动驾驶解决方案。 Tailor examples, pacing, and depth to this listener. Avoid analogies or references that assume knowledge outside this listener's profile.

    Voci dei presentatori
    Lenaplay
    Lenaplay
    Stile di apprendimento
    Divertente
    Fonti di conoscenza
    www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/
    link
    https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/
    docs.nvidia.com/dgx/dgxb200-user-guide/introduction-to-dgxb200.html
    link
    https://docs.nvidia.com/dgx/dgxb200-user-guide/introduction-to-dgxb200.html
    www.nvidia.com/en-us/data-center/dgx-b200/
    link
    https://www.nvidia.com/en-us/data-center/dgx-b200/
    www.nvidia.com/en-gb/data-center/h100/
    link
    https://www.nvidia.com/en-gb/data-center/h100/
    s201.q4cdn.com/141608511/files/doc_financials/2024/ar/NVIDIA-2024-Annual-Report.pdf
    link
    https://s201.q4cdn.com/141608511/files/doc_financials/2024/ar/NVIDIA-2024-Annual-Report.pdf

    Domande frequenti

    NVIDIA Blackwell is a specialized platform designed for the age of AI reasoning, moving beyond traditional pixel rendering to power modern AI factories. It is built to handle the immense computational demands of exascale AI by utilizing a unique architecture where two reticle-limited dies are connected by a ten terabyte per second chip-to-chip interconnect. This design allows the system to process massive amounts of data efficiently, making it possible to scale intelligence far beyond previous generations of GPU technology.

    Training trillion parameter models requires more than just increasing the number of GPUs; it requires a fundamental shift in how data moves through the system. NVIDIA Blackwell is specifically engineered to manage these massive models by providing the necessary throughput and connectivity to prevent processing delays. By reimagining the GPU as a platform for large-scale intelligence, Blackwell enables the development of complex AI models that were previously hindered by the sheer volume of data and parameters involved.

    The bandwidth wall is a performance bottleneck where chips sit idle while waiting for data to arrive, a common issue when trying to scale large AI workloads. NVIDIA Blackwell is designed to tear down this wall using its high-speed ten terabyte per second chip-to-chip interconnect. This ensures that data moves quickly enough to keep up with the processing power of the dies, allowing for the seamless training and deployment of trillion parameter models within modern AI factories.

    While traditional RTX cards and CUDA programming often focus on standard workstations or gaming, Blackwell represents a massive shift toward industrial-scale AI factories. It moves away from the traditional single-chip design to a dual-die architecture connected by high-speed links. This evolution is necessary for the age of AI reasoning, where the focus shifts from rendering pixels to building and scaling intelligence through massive datasets and complex neural networks that require specialized exascale infrastructure.

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    Parte di un piano di apprendimento

    Become a gpu engineer

    Become a gpu engineer

    PIANO DI APPRENDIMENTO

    Become a gpu engineer

    2 h 22 m•4 Episodi

    Punti chiave

    1

    The Dawn of Trillion Parameter Thinking

    0:00
    0:19
    0:42
    0:54
    1:16
    1:32
    2:02
    2:15
    2:34
    2

    Building the Multi-Trillion Parameter Pipeline

    2:42
    3:01
    3:13
    3:35
    3:43
    4:02
    4:12
    4:31
    4:40
    5:01
    5:14
    5:33
    5:48
    3

    The Traffic Controller of the AI Era

    6:00
    6:12
    6:34
    6:45
    7:09
    7:17
    7:39
    7:51
    8:02
    8:06
    8:28
    5:14
    8:54
    9:14
    9:31
    1:32
    4

    Guarding the Silicon Vault

    10:01
    5:14
    10:32
    10:43
    10:58
    11:02
    11:24
    11:35
    11:46
    5:14
    12:30
    12:38
    12:54
    13:05
    5

    From Desktops to Data Centers

    13:19
    13:29
    13:46
    13:53
    14:09
    14:21
    14:36
    3:13
    15:04
    15:23
    15:38
    7:17
    16:11
    6

    AI for the Physical World

    16:26
    16:44
    5:14
    17:05
    3:13
    17:29
    17:41
    17:55
    11:35
    18:27
    18:40
    18:54
    7:51
    19:19
    7

    Navigating the Blackwell Landscape

    19:28
    19:45
    5:14
    20:18
    20:33
    20:53
    21:03
    21:14
    21:23
    21:31
    21:39
    8

    Final Reflections on the AI Factory

    21:44
    21:56
    22:13
    22:26
    22:36
    22:44
    22:50

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