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  2. CuPy - Wikipedia

    en.wikipedia.org/wiki/CuPy

    CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. [3] CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU.

  3. CUDA - Wikipedia

    en.wikipedia.org/wiki/CUDA

    CUDA is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements for the execution of compute kernels. [6] In addition to drivers and runtime kernels, the CUDA platform includes compilers, libraries and developer tools to help programmers accelerate their applications.

  4. TensorFlow - Wikipedia

    en.wikipedia.org/wiki/TensorFlow

    TensorFlow includes an “eager execution” mode, which means that operations are evaluated immediately as opposed to being added to a computational graph which is executed later. [35] Code executed eagerly can be examined step-by step-through a debugger, since data is augmented at each line of code rather than later in a computational graph. [35]

  5. 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 ...

  6. PyTorch - Wikipedia

    en.wikipedia.org/wiki/PyTorch

    PyTorch 2.0 was released on 15 March 2023, introducing TorchDynamo, a Python-level compiler that makes code run up to 2x faster, along with significant improvements in training and inference performance across major cloud platforms.

  7. Nvidia Jetson - Wikipedia

    en.wikipedia.org/wiki/Nvidia_Jetson

    384-core Nvidia Volta architecture GPU with 48 Tensor cores 6-core Nvidia Carmel ARMv8.2 64-bit CPU 6MB L2 + 4MB L3 8 GiB 10–20W 2023 Jetson Orin Nano [20] 20–40 TOPS from 512-core Nvidia Ampere architecture GPU with 16 Tensor cores 6-core ARM Cortex-A78AE v8.2 64-bit CPU 1.5MB L2 + 4MB L3 4–8 GiB 7–10 W 2023 Jetson Orin NX 70–100 TOPS

  8. 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.

  9. Nvidia Tesla - Wikipedia

    en.wikipedia.org/wiki/Nvidia_Tesla

    Internal PCIe GPU (full-height, dual-slot) S2050 GPU Computing Server July 25, 2011 4× GF100 575 1792 1150 — GDDR5 4× 384 4× 3 [g] 3000 4× 148.4 No 4.122 2.061 2.0 900 1U rack-mount external GPUs, connect via 2× PCIe (×8 or ×16) S2070 GPU Computing Server July 25, 2011 — 4× 6 [g] No K10 GPU accelerator [16] Kepler: May 1, 2012 2× ...