A from-scratch PyTorch implementation of TurboQuant (ICLR 2026), Google's two-stage vector quantization algorithm for compressing LLM key-value caches — enhanced with a comprehensive, research-grade ...

Google has officially released TensorFlow 2.21. The most significant update in this release is the graduation of LiteRT from its preview stage to a fully production-ready stack. Moving forward, LiteRT ...

I lead an LLM pre-training team at Yandex and optimise large-scale distributed training runs. I lead an LLM pre-training team at Yandex and optimise large-scale distributed training runs. I lead an ...

Abstract: This paper presents the design of a framework for loading a pre-trained model in PyTorch on embedded devices to run local inference. Currently, TensorFlow Lite is the most widely used ...

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 ...

Your browser does not support the audio element. Walkthroughs, tutorials, guides, and tips. This story will teach you how to do something new or how to do something ...

Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...

The PyTorch team at Meta, stewards of the PyTorch open source machine learning framework, has unveiled Monarch, a distributed programming framework intended to bring the simplicity of PyTorch to ...

The choice between PyTorch and TensorFlow remains one of the most debated decisions in AI development. Both frameworks have evolved dramatically since their inception, converging in some areas while ...

According to Lex Fridman, major open source projects such as Linux, PyTorch, TensorFlow, and open-weight large language models (LLMs) are foundational to the current AI ecosystem, enabling rapid ...