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NVIDIA Developer published a step-by-step walkthrough showcasing the modern CUDA toolbox for optimizing GPU-accelerated code. The piece positions CUDA as a foundation for workloads from scientific simulations to large-scale AI training and demonstrates practical optimization guidance.
NVIDIA has released CUDA Python 1.0, presenting stable APIs and a unified foundation intended to give Python developers fuller access to CUDA and GPU capabilities without requiring C++ extension toolchains. The release aims to simplify building GPU-accelerated Python applications across the NVIDIA platform.
DeepSeek introduced DeepSeek-V3.2-Exp, an experimental model built on V3.1-Terminus that debuts DeepSeek Sparse Attention (DSA) to improve long-context efficiency with minimal quality loss; published benchmarks show parity with V3.1-Terminus. DeepSeek also cut DeepSeek API prices by over 50% effective immediately, provides TileLang and CUDA GPU kernels, and keeps V3.1-Terminus available via a temporary API until Oct 15, 2025, 15:59 UTC for comparison testing.
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