GPU
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Unexpected Vulkan Speedup in LLM Benchmarking
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Benchmarking local language models (LLMs) on a 3080 10GB GPU revealed that while CUDA generally outperforms Vulkan in token generation rates, certain models show unexpected speed improvements with Vulkan. Notably, the GLM4 9B Q6 model experienced a 2.2x speedup in prompt processing and a 1.7x speedup in token generation using Vulkan. Similarly, the Ministral3 14B 2512 Q4 model saw a significant 4.4x speedup in prompt processing and a 1.6x speedup in token generation. These findings suggest that Vulkan may offer performance benefits for specific models, particularly when partially offloaded to the GPU. This matters as it highlights potential optimizations for developers working with LLMs on different hardware configurations.
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TensorFlow 2.17 Updates
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TensorFlow 2.17 introduces significant updates, including a CUDA update that enhances performance on Ada-Generation GPUs like NVIDIA RTX 40**, L4, and L40, while dropping support for older Maxwell GPUs to keep Python wheel sizes manageable. The release also prepares for the upcoming TensorFlow 2.18, which will support Numpy 2.0, potentially affecting some edge cases in API usage. Additionally, TensorFlow 2.17 marks the last version to include TensorRT support, as future releases will no longer support it. These changes reflect ongoing efforts to optimize TensorFlow for modern hardware and software environments, ensuring better performance and compatibility.
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Four Ways to Run ONNX AI Models on GPU with CUDA
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Running ONNX AI models on GPUs with CUDA can be achieved through four distinct methods, enhancing flexibility and performance for machine learning operations. These methods include using ONNX Runtime with CUDA execution provider, leveraging TensorRT for optimized inference, employing PyTorch with its ONNX export capabilities, and utilizing the NVIDIA Triton Inference Server for scalable deployment. Each approach offers unique advantages, such as improved speed, ease of integration, or scalability, catering to different needs in AI model deployment. Understanding these options is crucial for optimizing AI workloads and ensuring efficient use of GPU resources.
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RPC-server llama.cpp Benchmarks
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The llama.cpp RPC server facilitates distributed inference of large language models (LLMs) by offloading computations to remote instances across multiple machines or GPUs. Benchmarks were conducted on a local gigabit network utilizing three systems and five GPUs, showcasing the server's performance in handling different model sizes and parameters. The systems included a mix of AMD and Intel CPUs, with GPUs such as GTX 1080Ti, Nvidia P102-100, and Radeon RX 7900 GRE, collectively providing a total of 53GB VRAM. Performance tests were conducted on various models, including Nemotron-3-Nano-30B and DeepSeek-R1-Distill-Llama-70B, highlighting the server's capability to efficiently manage complex computations across distributed environments. This matters because it demonstrates the potential for scalable and efficient LLM deployment in distributed computing environments, crucial for advancing AI applications.
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Enhancing AI Workload Observability with NCCL Inspector
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The NVIDIA Collective Communication Library (NCCL) Inspector Profiler Plugin is a tool designed to enhance the observability of AI workloads by providing detailed performance metrics for distributed deep learning training and inference tasks. It collects and analyzes data on collective operations like AllReduce and ReduceScatter, allowing users to identify performance bottlenecks and optimize communication patterns. With its low-overhead, always-on observability, NCCL Inspector is suitable for production environments, offering insights into compute-network performance correlations and enabling performance analysis, research, and production monitoring. By leveraging the plugin interface in NCCL 2.23, it supports various network technologies and integrates with dashboards for comprehensive performance visualization. This matters because it helps optimize the efficiency of AI workloads, improving the speed and accuracy of deep learning models.
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Distributed FFT in TensorFlow v2
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The recent integration of Distributed Fast Fourier Transform (FFT) in TensorFlow v2, through the DTensor API, allows for efficient computation of Fourier Transforms on large datasets that exceed the memory capacity of a single device. This advancement is particularly beneficial for image-like datasets, enabling synchronous distributed computing and enhancing performance by utilizing multiple devices. The implementation retains the original FFT API interface, requiring only a sharded tensor as input, and demonstrates significant data processing capabilities, albeit with some tradeoffs in speed due to communication overhead. Future improvements are anticipated, including algorithm optimization and communication tweaks, to further enhance performance. This matters because it enables more efficient processing of large-scale data in machine learning applications, expanding the capabilities of TensorFlow.
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Reducing CUDA Binary Size for cuML on PyPI
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Starting with the 25.10 release, cuML can now be easily installed via pip from PyPI, eliminating the need for complex installation steps and Conda environments. The NVIDIA team has successfully reduced the size of CUDA C++ library binaries by approximately 30%, enabling this distribution method. This reduction was achieved through optimization techniques that address bloat in the CUDA C++ codebase, making the libraries more accessible and efficient. These efforts not only improve user experience with faster downloads and reduced storage requirements but also lower distribution costs and promote the development of more manageable CUDA C++ libraries. This matters because it simplifies the installation process for users and encourages broader adoption of cuML and similar libraries.
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TensorFlow 2.15: Key Updates and Enhancements
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TensorFlow 2.15 introduces several key updates, including a simplified installation process for NVIDIA CUDA libraries on Linux, which now allows users to install necessary dependencies directly through pip, provided the NVIDIA driver is already installed. For Windows users, oneDNN CPU performance optimizations are now enabled by default, enhancing TensorFlow's efficiency on x86 CPUs. The release also expands the capabilities of tf.function, offering new types such as tf.types.experimental.TraceType and tf.types.experimental.FunctionType for better input handling and function representation. Additionally, TensorFlow packages are now built with Clang 17 and CUDA 12.2, optimizing performance for NVIDIA Hopper-based GPUs. These updates are crucial for developers seeking improved performance and ease of use in machine learning applications.
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Solving Large-Scale Linear Sparse Problems with cuDSS
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The NVIDIA CUDA Direct Sparse Solver (cuDSS) is designed to tackle large-scale linear sparse problems in fields like Electronic Design Automation (EDA) and Computational Fluid Dynamics (CFD), which are becoming increasingly complex. cuDSS offers unprecedented scalability and performance by allowing users to run sparse solvers at a massive scale with minimal code changes. It leverages hybrid memory mode to utilize both CPU and GPU resources, enabling the handling of larger problems that exceed a single GPU's memory capacity. This approach allows for efficient computation even for problems with over 10 million rows and a billion nonzeros, by using 64-bit integer indexing arrays and optimizing memory usage across multiple GPUs or nodes. Hybrid memory mode in cuDSS addresses the memory limitations of a single GPU by using both CPU and GPU memories, albeit with a trade-off in data transfer time due to bus bandwidth. This mode is not enabled by default, but once activated, it allows the solver to manage device memory automatically or with user-defined limits. The performance of hybrid memory mode is influenced by the CPU/GPU memory bandwidth, but modern NVIDIA driver optimizations and fast interconnects help mitigate these impacts. By setting memory limits and utilizing the maximum GPU memory, users can achieve optimal performance, making it possible to solve larger problems efficiently. For even larger computational tasks, cuDSS supports multi-GPU mode (MG mode) and Multi-GPU Multi-Node (MGMN) mode, which allow the use of all GPUs in a node or across multiple nodes, respectively. MG mode simplifies the process by handling GPU communications internally, eliminating the need for developers to manage distributed communication layers. MGMN mode, on the other hand, requires a communication layer like Open MPI or NCCL, enabling the distribution of computations across multiple nodes. These modes allow for solving massive problems or speeding up computations by utilizing more GPUs, thereby accommodating the growing size and complexity of real-world problems. This matters because it provides a scalable solution for industries facing increasingly complex computational challenges.
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NCP-GENL Study Guide: NVIDIA Certified Pro – Gen AI LLMs
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The NVIDIA Certified Professional – Generative AI LLMs 2026 certification is designed to validate expertise in deploying and managing large language models (LLMs) using NVIDIA's AI technologies. This certification focuses on equipping professionals with the skills needed to effectively utilize NVIDIA's hardware and software solutions to optimize the performance of generative AI models. Key areas of study include understanding the architecture of LLMs, deploying models on NVIDIA platforms, and fine-tuning models for specific applications. Preparation for the NCP-GENL certification involves a comprehensive study of NVIDIA's AI ecosystem, including the use of GPUs for accelerated computing and the integration of software tools like TensorRT and CUDA. Candidates are expected to gain hands-on experience with NVIDIA's frameworks, which are essential for optimizing model performance and ensuring efficient resource management. The study guide emphasizes practical knowledge and problem-solving skills, which are critical for managing the complexities of generative AI systems. Achieving the NCP-GENL certification offers professionals a competitive edge in the rapidly evolving field of AI, as it demonstrates a specialized understanding of cutting-edge technologies. As businesses increasingly rely on AI-driven solutions, certified professionals are well-positioned to contribute to innovative projects and drive technological advancements. This matters because it highlights the growing demand for skilled individuals who can harness the power of generative AI to create impactful solutions across various industries.