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    NVIDIA Blackwell: Scaling Exascale AI and Trillion Parameter Models

    23 min
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    11 мая 2026 г.
    Technology

    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

    Лучшая цитата из 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.

    ”
    H

    Generated by Howard Zane

    Вопрос для ввода

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    Источники знаний
    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

    Часто задаваемые вопросы

    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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    JAX: XLA, Pallas & Custom Backends
    ПЛАН ОБУЧЕНИЯ

    JAX: XLA, Pallas & Custom Backends

    3 h 18 m•4 Эпизоды

    Ключевые выводы

    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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    Learn DL, ML, GenAI & FastAPI Backend

    As AI moves from research to production, the demand for engineers who can both build models and deploy them is skyrocketing. This plan is ideal for aspiring full-stack AI developers looking to bridge the gap between data science and scalable backend engineering.

    4 h 41 m•4 Разделы