Browsing: Nvidia

Microsoft, Nvidia, and MediaTek are working together on “RTX Spark,” a new chip for Windows PCs that’s all about AI, but if you look beyond that,…

Nvidia does more than make AI chips. While it’s true that their chips power the data centers that run most of the large language models (which…

Linear attention replaces the unbounded KV cache of softmax attention with a fixed-size recurrent state. This cuts sequence mixing to linear time and decoding to constant…

AMD might have the solution if you like the idea of Nvidia’s DGX Spark as an AI workstation, but balk at having to use a specialized…

NVIDIA researchers have released Nemotron-Labs-Diffusion, a language model family that unifies three decoding modes in one architecture. The model supports autoregressive (AR) decoding, diffusion-based parallel decoding,…

Pretraining frontier-scale LLMs in FP8 is now standard practice, but moving to 4-bit floating point has remained an open research problem because narrower formats compress dynamic…

World models (systems that synthesize realistic video sequences from an initial image and a set of actions) are becoming central to embodied AI, simulation, and robotics…

Scaling large language models (LLMs) is expensive. Every token processed during inference and every gradient computed during training flows through feedforward layers that account for over…

Step 01 of 09  ·  Prerequisites What You Need Before You Start cuda-oxide has specific version requirements for each dependency. Before installing anything, verify your system…

Training a family of large language models (LLMs) has always come with a painful multiplier: every model variant in the family—whether 8B, 30B, or 70B—typically requires…