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  2. AMD Instinct - Wikipedia

    en.wikipedia.org/wiki/AMD_Instinct

    AMD Instinct is AMD's brand of data center GPUs. [1] [2] It replaced AMD's FirePro S brand in 2016.Compared to the Radeon brand of mainstream consumer/gamer products, the Instinct product line is intended to accelerate deep learning, artificial neural network, and high-performance computing/GPGPU applications.

  3. Nvidia DGX - Wikipedia

    en.wikipedia.org/wiki/Nvidia_DGX

    Announced March 2024, GB200 NVL72 connects 36 Grace Neoverse V2 72-core CPUs and 72 B100 GPUs in a rack-scale design. The GB200 NVL72 is a liquid-cooled, rack-scale solution that boasts a 72-GPU NVLink domain that acts as a single massive GPU . Nvidia DGX GB200 offers 13.5 TB HBM3e of shared memory with linear scalability for giant AI models ...

  4. Tensor Processing Unit - Wikipedia

    en.wikipedia.org/wiki/Tensor_Processing_Unit

    In 2021, Google revealed the physical layout of TPU v5 is being designed with the assistance of a novel application of deep reinforcement learning. [35] Google claims TPU v5 is nearly twice as fast as TPU v4, [ 36 ] and based on that and the relative performance of TPU v4 over A100, some speculate TPU v5 as being as fast as or faster than an H100 .

  5. Tegra - Wikipedia

    en.wikipedia.org/wiki/Tegra

    It contains 7 billion transistors and 8 custom ARMv8 cores, a Volta GPU with 512 CUDA cores, an open sourced TPU (Tensor Processing Unit) called DLA (Deep Learning Accelerator). [132] [133] It is able to encode and decode 8K Ultra HD (7680×4320). Users can configure operating modes at 10 W, 15 W, and 30 W TDP as needed and the die size is 350 ...

  6. RDNA 3 - Wikipedia

    en.wikipedia.org/wiki/RDNA_3

    The development of RDNA 3's chiplet architecture began towards the end of 2017 with Naffziger leading the AMD graphics team in the effort. [7] The benefit of using chiplets is that dies can be fabricated on different process nodes depending on their functions and intended purpose. According to Naffziger, cache and SRAM do not scale as linearly ...

  7. Neural processing unit - Wikipedia

    en.wikipedia.org/wiki/Neural_processing_unit

    A neural processing unit (NPU), also known as AI accelerator or deep learning processor, is a class of specialized hardware accelerator [1] or computer system [2] [3] designed to accelerate artificial intelligence (AI) and machine learning applications, including artificial neural networks and computer vision.

  8. Transformer (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Transformer_(deep_learning...

    The plain transformer architecture had difficulty converging. In the original paper [1] the authors recommended using learning rate warmup. That is, the learning rate should linearly scale up from 0 to maximal value for the first part of the training (usually recommended to be 2% of the total number of training steps), before decaying again.

  9. GeForce RTX 40 series - Wikipedia

    en.wikipedia.org/wiki/GeForce_RTX_40_series

    The GeForce 40 series is a family of consumer graphics processing units (GPUs) developed by Nvidia as part of its GeForce line of graphics cards, succeeding the GeForce 30 series. The series was announced on September 20, 2022, at the GPU Technology Conference, and launched on October 12, 2022, starting with its flagship model, the RTX 4090. [1]

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