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6 articles
Nvidia’s real competitive advantage isn’t its chips, but its CUDA software platform, which has become the backbone of AI development. As AI evolves, this software moat is becoming even more critical.
This article explains CuPy, a GPU-accelerated Python library for high-performance numerical computing, and how it leverages CUDA kernels, streams, and sparse matrices for machine learning workloads.
NVIDIA has released cuda-oxide, an experimental Rust-to-CUDA compiler backend that enables direct compilation of SIMT GPU kernels to PTX bytecode, streamlining GPU development for Rust developers.
Nvidia's CUDA software platform has become a critical competitive advantage, creating a formidable barrier that rivals traditional hardware superiority in the AI industry.
Learn to implement sparse matrix operations using CUDA kernels to achieve 20.5% inference and 21.9% training speedup in LLMs, following the TwELL approach by Sakana AI and NVIDIA.
Learn how to run a tiny but powerful AI model called Bonsai 1-bit LLM on your computer using CUDA and GGUF technology.
