That means developers will soon be able to run MLX models directly on NVIDIA GPUs, which is a pretty big deal. Here’s why.

Sparse matrix multiplication is widely used in various practical applications. Different accelerators have been proposed to speed up sparse matrix-dense vector multiplication (SpMV), sparse ...

Only twice in TIOBE Programming Index history has a language commanded such a high percentage of developers’ interest.

Discover how nvmath-python leverages NVIDIA CUDA-X math libraries for high-performance matrix operations, optimizing deep learning tasks with epilog fusion, as detailed by Szymon Karpiński.

Conclusion nvmath-python represents a significant advancement in leveraging NVIDIA’s powerful math libraries within Python environments. By fusing epilog operations with matrix multiplication, it ...

For the second time since March, a cybersecurity firm has discovered troubling malware software packages uploaded to the Python Package Index platform.

Researchers upend AI status quo by eliminating matrix multiplication in LLMs Running AI models without floating point matrix math could mean far less power consumption.

Matrix multiplication (MatMul) is a fundamental operation in most neural networks, primarily because GPUs are highly optimized for these computations. Despite its critical role in deep learning, ...

algorithms New Breakthrough Brings Matrix Multiplication Closer to Ideal By eliminating a hidden inefficiency, computer scientists have come up with a new way to multiply large matrices that’s faster ...