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    How to Implement Tensor Parallelism for Faster Inference

    Implementing tensor parallelism accelerates large language model (LLM) inference by distributing computations across GPUs, reducing latency for real-world applications. Below is a structured breakdown of key insights and practical considerations for developers: Benefits: Challenges:
    Thumbnail Image of Tutorial How to Implement Tensor Parallelism for Faster Inference
    Image of Author Dr. Dipen
    Dr. Dipen
    I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
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      Retrieval‑Augmented Model Enhances TRIZ‑Based Patent Entity Recognition

      The retrieval-augmented model outperforms traditional TRIZ-based patent entity recognition methods by integrating dynamic contextual data during analysis. Traditional approaches rely on static rule-based systems or limited training datasets, which struggle with evolving patent terminology and…
      Thumbnail Image of Tutorial Retrieval‑Augmented Model Enhances TRIZ‑Based Patent Entity Recognition
      Image of Author Dr. Dipen
      Dr. Dipen
      I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
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        Using Sharpness-Aware Minimization to Boost Deep Learning Models

        Sharpness-Aware Minimization (SAM) is an optimization technique designed to improve the generalization of deep learning models by flattening the loss landscape during training. Unlike traditional methods like Stochastic Gradient Descent (SGD) or Adam, SAM explicitly balances minimizing the loss and…
        Thumbnail Image of Tutorial Using Sharpness-Aware Minimization to Boost Deep Learning Models
        Image of Author Dr. Dipen
        Dr. Dipen
        I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
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          Tensor Parallelism Checklist: Maximize GPU Utilization

          Tensor parallelism splits model computations across GPUs to boost efficiency. Below is a comparison of key techniques: Tensor parallelism improves training speed by 2–4x compared to single-GPU setups, as seen in vLLM benchmarks. It also enhances model accuracy by maintaining full-precision…
          Thumbnail Image of Tutorial Tensor Parallelism Checklist: Maximize GPU Utilization
          Image of Author Dr. Dipen
          Dr. Dipen
          I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
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            What Is Tensor Parallelism and How to Apply It

            Watch: Scale ANY Model: PyTorch DDP, ZeRO, Pipeline & Tensor Parallelism Made Simple (2025 Guide) by Zachary Mueller Tensor Parallelism (TP) is a distributed computing strategy that splits large model tensors across multiple GPUs to reduce memory usage and accelerate training/inference. Unlike Data…
            Thumbnail Image of Tutorial What Is Tensor Parallelism and How to Apply It
            Image of Author Dr. Dipen
            Dr. Dipen
            I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
            •Last Updated:
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