TPUv7 offers a viable alternative to the GPU-centric AI stack has already arrived — one with real implications for the economics and architecture of frontier-scale training.

Ben Koska, Founder and CEO of SF Tensor, is an AI researcher and systems engineer known for his work on high-performance ...

Programming model moves from managing thousands of low-level threads to working with high-level ‘tiles of data’ ...

JAX is one of the fastest-growing tools in machine learning, and this video breaks it down in just 100 seconds. We explain how JAX uses XLA, JIT compilation, and auto-vectorization to turn ordinary ...

Google’s TPUs cannot dethrone Nvidia’s GPUs. But, there is a bigger challenge that can seriously threaten Nvidia’s growth trajectory.

Nvidia's AI monopoly fractures as Google Gemini 3 and Anthropic Claude 4.5 defect to custom TPUs. This seismic shift pressures margins and ends the GPU era.

When the FORTRAN programming language debuted in 1957, it transformed how scientists and engineers programmed computers. Complex calculations could suddenly be expressed in concise, math-like notation ...

The $12K machine promises AI performance can scale to 32 chip servers and beyond but an immature software stack makes harnessing that compute challenging ...

TPUs are Google’s specialized ASICs built exclusively for accelerating tensor-heavy matrix multiplication used in deep learning models. TPUs use vast parallelism and matrix multiply units (MXUs) to ...

According to DeepLearning.AI (@DeepLearningAI), the new PyTorch for Deep Learning Professional Certificate, led by Laurence Moroney, provides in-depth, practical training on building, optimizing, and ...