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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.
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        Agent‑Centric Benchmarking Moves Beyond Static Datasets

        Agent-centric benchmarking transforms how AI systems are evaluated by replacing static datasets with dynamic, interactive protocols. Traditional benchmarks rely on fixed datasets with predefined questions or tasks, limiting their ability to test real-world adaptability. In contrast, agent-centric…
        Thumbnail Image of Tutorial Agent‑Centric Benchmarking Moves Beyond Static Datasets
        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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          Ask What Explanations Should Answer, Not If Model Is Interpretable

          Watch: Interpretable vs Explainable Machine Learning by A Data Odyssey When working with AI models, the focus should shift from whether a model is interpretable to what questions explanations must answer. As mentioned in the Why Explanations Matter in AI Development section, explanations bridge the…
          Thumbnail Image of Tutorial Ask What Explanations Should Answer, Not If Model Is Interpretable
          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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            Benchmark for checking scientific references produced by LLMs

            Watch: CiteAudit: Benchmark to Detect Fake Citations by AI Research Roundup Creating a benchmark for scientific references generated by large language models (LLMs) requires careful evaluation of accuracy, relevance, and reproducibility. Below is a structured comparison of existing benchmarks and…
            Thumbnail Image of Tutorial Benchmark for checking scientific references produced by LLMs
            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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