Browsing: Nvidia

Knowledge distillation (KD) transfers “dark knowledge” from a large teacher model to a smaller student. The student learns from the teacher’s full output probability distribution over…

Reinforcement learning for language agents is growing more complex. Agents now manage multi-turn tool use, long-running contexts, and multi-agent orchestration. The main engineering challenge is connecting…

CLIENT_SCRIPT += r”’ def main(): p = argparse.ArgumentParser() p.add_argument(“–num_sites”, type=int, default=3) p.add_argument(“–alpha”, type=float, default=0.3) p.add_argument(“–local_epochs”, type=int, default=1) p.add_argument(“–mu”, type=float, default=0.0) p.add_argument(“–max_samples”, type=int, default=4000) p.add_argument(“–batch_size”, type=int, default=64)…

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…

The Nvidia Vera chip is rarely the headline when earnings beat estimates, but it should be. When Nvidia reported Q1 revenue of US$81.62 billion on Wednesday,…

Alibaba has unveiled a new AI processor built specifically for AI agents, pairing the chip announcement with a multi-year silicon roadmap and a new large language…

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,…

President Trump flew to Beijing, brought Jensen Huang along at the last minute, and left two days later, telling reporters that “something could happen” on chip…

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…