Browsing: Nvidia

In this tutorial, we build a complete end-to-end pipeline using NVIDIA Model Optimizer to train, prune, and fine-tune a deep learning model directly in Google Colab.…

Run Google’s latest omni-capable open models faster on NVIDIA RTX AI PCs, from NVIDIA Jetson Orin Nano, GeForce RTX desktops to the new DGX Spark, to…

Nearly all graphics cards are expensive right now, but the ongoing RAM shortage is at least partly to blame for the current state of things. But,…

NVIDIA researchers introduced ProRL AGENT, a scalable infrastructure designed for reinforcement learning (RL) training of multi-turn LLM agents. By adopting a ‘Rollout-as-a-Service’ philosophy, the system decouples…

Post-training Large Language Models (LLMs) for long-horizon agentic tasks—such as software engineering, web browsing, and complex tool use—presents a persistent trade-off between computational efficiency and model…

On a Monday episode of the Lex Fridman podcast, Nvidia CEO Jensen Huang made a hot-button statement: “I think we’ve achieved AGI.”AGI, or artificial general intelligence,…

NVIDIA has announced the release of Nemotron-Cascade 2, an open-weight 30B Mixture-of-Experts (MoE) model with 3B activated parameters. The model focuses on maximizing ‘intelligence density,’ delivering…

Nemotron 3 Super is now available as a fully managed and serverless model on Amazon Bedrock, joining the Nemotron Nano models that are already available within…

The deployment of autonomous AI agents—systems capable of using tools and executing code—presents a unique security challenge. While standard LLM applications are restricted to text-based interactions,…

angles = np.linspace(0.0, 2.0 * np.pi, n_particles, endpoint=False, dtype=np.float32) px0_np = 0.4 * np.cos(angles).astype(np.float32) py0_np = (0.7 + 0.15 * np.sin(angles)).astype(np.float32) vx0_np = (-0.8 * np.sin(angles)).astype(np.float32)…