NVIDIA GPUs
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SteamOS Expands to Arm Devices, Broadening Gaming Options
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Valve is expanding SteamOS to support Arm-based devices, potentially transforming the landscape of PC gaming by enabling gaming on a wider range of hardware, including handhelds. This development could lead to a significant increase in hardware options with native SteamOS support, offering more choices across different price and performance levels. While Arm-based chipsets are becoming more competitive, especially in lower power segments, challenges remain for desktop PC gamers using Nvidia GPUs due to the early stage of open-source driver integration. This expansion signifies a move towards more inclusive and versatile gaming experiences beyond traditional x86 platforms. Why this matters: The expansion of SteamOS to Arm devices could democratize access to PC gaming, offering more affordable and varied hardware options to a broader audience.
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RTX 5090 CuPy Setup: Blackwell Architecture & CUDA 13.1
Read Full Article: RTX 5090 CuPy Setup: Blackwell Architecture & CUDA 13.1![[D] RTX 5090 / 50-series CuPy setup (Blackwell architecture, CUDA 13.1 required)](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-9252-300x171.png)
Users experiencing issues with CuPy on RTX 5090, 5080, or 5070 GPUs should note that the new Blackwell architecture requires CUDA 13.1 for compatibility. Pre-built CuPy wheels do not support the compute capability of these GPUs, necessitating a build from source. After uninstalling existing CuPy versions, install the CUDA Toolkit 13.1 and then CuPy without binaries. For Windows users, ensure the correct path is added to the system PATH. Proper configuration can lead to significant performance improvements, such as a 21× speedup in physics simulations compared to CPU processing. This matters because it highlights the importance of proper software setup to fully utilize the capabilities of new hardware.
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Quick Start Guide for LTX-2 on NVIDIA GPUs
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Lightricks has launched LTX-2, a cutting-edge local AI model for video creation that rivals top cloud-based models by producing up to 20 seconds of 4K video with high visual quality. Designed to work optimally with NVIDIA GPUs in ComfyUI, a quick start guide is available to help users maximize performance, including tips on settings and VRAM usage. This release is part of a broader announcement from CES 2026, which also highlighted improvements in ComfyUI, enhancements in inference performance for llama.cpp and Ollama, and new AI features in Nexa.ai's Hyperlink. These advancements signify a leap forward in accessible, high-quality AI-driven video production.
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OpenCV 4.13: Enhanced AVX-512 and CUDA 13 Support
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OpenCV 4.13 introduces enhanced support for AVX-512, a set of instructions that can significantly boost performance on compatible hardware, making it more efficient for tasks such as image processing. The update also includes support for CUDA 13, enabling better integration with NVIDIA's latest GPU technologies, which is crucial for accelerating computer vision applications. Additionally, the release brings a variety of other improvements and new features, including bug fixes and optimizations, to further enhance the library's capabilities. These advancements are important as they enable developers to leverage cutting-edge hardware and software optimizations for more efficient and powerful computer vision solutions.
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Advanced Quantum Simulation with cuQuantum SDK v25.11
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Simulating large-scale quantum computers is increasingly challenging as quantum processing units (QPUs) improve, necessitating advanced techniques to validate results and generate datasets for AI models. The cuQuantum SDK v25.11 introduces new components to accelerate workloads like Pauli propagation and stabilizer simulations using NVIDIA GPUs, crucial for simulating quantum circuits and managing quantum noise. Pauli propagation efficiently simulates observables in large-scale circuits by dynamically discarding insignificant terms, while stabilizer simulations leverage the Gottesman-Knill theorem for efficient classical simulation of Clifford group gates. These advancements are vital for quantum error correction, verification, and algorithm engineering, offering significant speedups over traditional CPU-based methods. Why this matters: Enhancing quantum simulation capabilities is essential for advancing quantum computing technologies and ensuring reliable, scalable quantum systems.
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Accelerating Inference with Skip Softmax in TensorRT-LLM
Read Full Article: Accelerating Inference with Skip Softmax in TensorRT-LLM
Skip Softmax is a technique designed to accelerate long-context inference in large language models (LLMs) by optimizing the attention computation process. It achieves this by dynamically pruning attention blocks that contribute minimally to the output, thereby reducing computation time without the need for retraining. This method is compatible with existing models and leverages NVIDIA's Hopper and Blackwell GPUs for enhanced performance, offering up to 1.4x speed improvements in both time-to-first-token and time-per-output-token. Skip Softmax maintains accuracy while providing substantial efficiency gains, making it a valuable tool for machine learning engineers working with long-context scenarios. This matters because it addresses the critical bottleneck of attention computation, enabling faster and more efficient deployment of LLMs at scale.