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Tag: gpu

  • Tensor Reshaping with PyTorch and CUDA

    Tensor Reshaping with PyTorch and CUDA Tensor Reshaping involves changing the shape of a tensor without altering its underlying data. This operation is frequently used to prepare tensors for different operations in neural networks and other numerical computations. While the reshaping operation itself is typically not computationally intensive, performing it on a GPU using CUDA Read more

  • Matrix Multiplication with PyTorch and CUDA

    Matrix Multiplication with PyTorch and CUDA Matrix Multiplication is a fundamental operation in linear algebra and is crucial in many machine learning algorithms, especially in the layers of neural networks. CUDA significantly accelerates this operation by parallelizing the numerous multiply-accumulate operations involved. Code Example with PyTorch and CUDA import torch # Check if CUDA is Read more

  • Accelerating Image Classification with CUDA

    Image Classification using CUDA CUDA (Compute Unified Device Architecture) significantly accelerates image classification tasks by leveraging the parallel processing power of NVIDIA GPUs. Deep learning models, which are commonly used for image classification, involve numerous matrix operations that are highly parallelizable and thus benefit greatly from GPU acceleration via CUDA. How CUDA Accelerates Image Classification Read more

  • CUDA vs. ROCm for LLM Training

    CUDA vs. ROCm CUDA (Compute Unified Device Architecture) and ROCm (Radeon Open Compute) are the two primary software platforms for General-Purpose computing on Graphics Processing Units (GPGPU) used in accelerating computationally intensive tasks, including the training of Large Language Models (LLMs). CUDA is developed by NVIDIA and is designed for their GPUs, while ROCm is Read more

  • How CUDA Solves Transcendental Functions

    How CUDA Solves Transcendental Functions CUDA leverages the parallel processing power of NVIDIA GPUs to efficiently compute transcendental functions (like sine, cosine, logarithm, exponential, etc.). It achieves this through a combination of dedicated hardware units and optimized software implementations within its math libraries. 1. Special Function Units (SFUs) Modern NVIDIA GPUs include Special Function Units Read more

  • Exploring CUDA (Compute Unified Device Architecture)

    Exploring CUDA CUDA is a parallel computing platform and programming model developed by NVIDIA for use with their GPUs. It allows software developers to leverage the massive parallel processing power of NVIDIA GPUs for general-purpose computing tasks, significantly accelerating applications beyond traditional CPU-bound processing. 1. CUDA Architecture: The Hardware Foundation NVIDIA GPUs are designed with Read more

  • Can AMD GPUs Train LLMs?

    Can AMD GPUs Train LLMs? AMD GPUs can be used to train Large Language Models (LLMs). While NVIDIA GPUs, particularly those with CUDA architecture, have historically dominated the LLM training landscape, AMD has been making significant strides in this area with its ROCm (Radeon Open Compute) platform. 1. ROCm Platform ROCm is AMD’s open-source software Read more

  • AMD GPUs vs. NVIDIA GPUs for LLM Training

    AMD GPUs vs. NVIDIA GPUs for LLM Training Here we dive into how AMD GPUs can be used for LLM training, and compare them directly with the dominant player in this field: NVIDIA GPUs. Comparison: AMD vs. NVIDIA GPUs for LLM Training Feature NVIDIA GPUs AMD GPUs Dominant Architecture/Platform CUDA (Compute Unified Device Architecture) – Read more

  • How GPU Architecture revolutionized LLMs

    How GPU Architecture Helped LLMs The development and advancement of Large Language Models (LLMs) have been significantly propelled by the unique architecture of Graphics Processing Units (GPUs). Their parallel processing capabilities, high memory bandwidth, and specialized compute units have made training and deploying these massive models feasible and efficient. 1. Massively Parallel Processing LLMs involve Read more

  • Competition Between NVIDIA and Broadcom Offerings

    NVIDIA vs. Broadcom: Competition (April 2025) Historical Differentiation NVIDIA: Pioneered & dominates the general-purpose GPU market, with a strong foothold in AI, gaming, & professional visualization. Their CUDA platform is a significant barrier to entry. Broadcom: Traditionally a leader in custom ASICs for networking & communication infrastructure. Their entry into custom AI silicon leverages their Read more