numerical computations
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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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Solar 100B’s Counting Claims Surpass GPT
Read Full Article: Solar 100B’s Counting Claims Surpass GPT
Solar 100B has made a bold claim that its counting capabilities surpass those of GPT models currently available. This assertion highlights the advancements in AI technology, particularly in specific tasks such as numerical computations. Such developments could have significant implications for industries that rely heavily on accurate data processing and analysis. Understanding these advancements is crucial as they could lead to more efficient and reliable AI applications in the future.
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BareGPT: A Numpy-Based Transformer with Live Attention
Read Full Article: BareGPT: A Numpy-Based Transformer with Live Attention
BareGPT is a new transformer model similar to NanoGPT, implemented entirely in Numpy, offering a unique approach to machine learning with live attention visualization. This development showcases the versatility of Numpy in creating efficient machine learning models without relying on more complex frameworks. The transformer model provides insights into attention mechanisms, which are crucial for understanding how models process and prioritize input data. This matters because it highlights the potential for simpler, more accessible tools in machine learning, making advanced techniques more approachable for a broader audience.
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TensorFlow 2.18: Key Updates and Changes
Read Full Article: TensorFlow 2.18: Key Updates and Changes
TensorFlow 2.18 introduces several significant updates, including support for NumPy 2.0, which may affect some edge cases due to changes in type promotion rules. While most TensorFlow APIs are compatible with NumPy 2.0, developers should be aware of potential conversion errors and numerical changes in results. To assist with this transition, TensorFlow has updated certain tensor APIs to maintain compatibility with NumPy 2.0 while preserving previous conversion behaviors. Developers are encouraged to consult the NumPy 2 migration guide to navigate these changes effectively. The release also marks a shift in the development of LiteRT, formerly known as TFLite. The codebase is being transitioned to LiteRT, and once complete, contributions will be accepted directly through the new LiteRT repository. This change means that binary TFLite releases will no longer be available, prompting developers to switch to LiteRT for the latest updates and developments. This transition aims to streamline development and foster more direct contributions from the community. TensorFlow 2.18 enhances GPU support with dedicated CUDA kernels for GPUs with a compute capability of 8.9, optimizing performance for NVIDIA's Ada-Generation GPUs like the RTX 40 series. However, to manage Python wheel sizes, support for compute capability 5.0 has been discontinued, making the Pascal generation the oldest supported by precompiled packages. Developers using Maxwell GPUs are advised to either continue using TensorFlow 2.16 or compile TensorFlow from source, provided the CUDA version supports Maxwell. This matters because it ensures TensorFlow remains efficient and up-to-date with the latest hardware advancements while maintaining flexibility for older systems.