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  2. Tesla (microarchitecture) - Wikipedia

    en.wikipedia.org/wiki/Tesla_(microarchitecture)

    In this case the formula to calculate the theoretical performance in floating point operations per second becomes: FLOPS sp = 2 × n × f. The theoretical double-precision processing power of a Tesla GPU is 1/8 of the single precision performance on GT200; there is no double precision support on G8x and G9x. [9]

  3. DeepSpeed - Wikipedia

    en.wikipedia.org/wiki/DeepSpeed

    Features include mixed precision training, single-GPU, multi-GPU, and multi-node training as well as custom model parallelism. The DeepSpeed source code is licensed under MIT License and available on GitHub. [5] The team claimed to achieve up to a 6.2x throughput improvement, 2.8x faster convergence, and 4.6x less communication. [6]

  4. 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. [25] [26]

  5. Mamba (deep learning architecture) - Wikipedia

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

    [6] [2] This enables Mamba to selectively focus on relevant information within sequences, effectively filtering out less pertinent data. The model transitions from a time-invariant to a time-varying framework, which impacts both computation and efficiency. [2] [7]

  6. Nvidia Tesla - Wikipedia

    en.wikipedia.org/wiki/Nvidia_Tesla

    Nvidia Tesla C2075. Offering computational power much greater than traditional microprocessors, the Tesla products targeted the high-performance computing market. [4] As of 2012, Nvidia Teslas power some of the world's fastest supercomputers, including Summit at Oak Ridge National Laboratory and Tianhe-1A, in Tianjin, China.

  7. Model collapse - Wikipedia

    en.wikipedia.org/wiki/Model_collapse

    Model collapse in generative models is reduced when data accumulates. Some researchers and commentators on model collapse warn that the phenomenon could fundamentally threaten future generative AI development: As AI-generated data is shared on the Internet, it will inevitably end up in future training datasets, which are often crawled from the Internet.

  8. Tesla Dojo - Wikipedia

    en.wikipedia.org/wiki/Tesla_Dojo

    Tesla Dojo is a supercomputer designed and built by Tesla for computer vision video processing and recognition. [1] It is used for training Tesla's machine learning models to improve its Full Self-Driving (FSD) advanced driver-assistance system. According to Tesla, it went into production in July 2023. [2]

  9. Roofline model - Wikipedia

    en.wikipedia.org/wiki/Roofline_model

    The roofline model is an intuitive visual performance model used to provide performance estimates of a given compute kernel or application running on multi-core, many-core, or accelerator processor architectures, by showing inherent hardware limitations, and potential benefit and priority of optimizations.