How NVIDIA Uses Hugging Face

Teams customize their models to hit their targets for latency, speed, memory, and compute. With the open NVIDIA Nemotron family of models…

Alibaba released the open weights for Qwen3.8-2.4T-A95B (Qwen3.8-Max), its largest open-weight model, bringing near-frontier capabilities to the open ecosystem.

Long-running AI agents spend most of their time on high-volume execution: tool calls, result validation, and subagent delegation. Using a frontier reasoning…

Learn how NVIDIA NeMo Switchyard routes AI agent workloads across models using tuning-free and tunable routers that balance model capability, cost, and latency.

Meta returns to the open source ecosystem with the release of Muse Glimmer, a 30B open-weight dense model with a 120K+ context window built for local AI agentic…

A central challenge in robotics is building policies that generalize beyond the demonstrations they’re trained on. A policy that succeeds in a training scene…

Autonomous vehicle (AV) development often relies on separate models for trajectory generation, high-level intent prediction, scene understanding…

Unlike autonomous driving or industrial robotics, healthcare robotics can’t rely on internet-scale data collection or unlimited real-world experimentation.

NVIDIA Ising Calibration is an open source vision language model (VLM) designed to interpret diagnostic outputs from quantum processors and determine how they…

Modern chip design is increasingly limited by engineering time. Register transfer level (RTL) development and verification require specialized hardware…

Customization is what enables developers to take a general model and tailor it to use cases, domains, languages, and more. However, customization comes with a…

Developers building video analytics applications across large spaces must track the same object as it moves between camera views. Single-camera 2D tracking…

Coding AI agents are becoming practical operators for long-running machine learning (ML) workflows. They can inspect repositories, set up runtimes…

What if autonomous coding AI agents could push your vision reasoning models above 90% accuracy with almost no manual effort? When adapting vision reasoning…

Fine-tuning LLMs for financial natural language processing (NLP) is constrained by limited, imbalanced data. Real-world financial news overrepresents earnings…

As more teams move from humanoid robot bring-up to task-specific skill development, the need for repeatable development workflows is growing.

Industrial machinery generates more alarms than technicians can triage. For each important alarm requiring follow-up, the technician pulls historical context…

As context windows grow longer, moving large model weights efficiently becomes critical to performance. A common way to address this is quantization…

As AI systems move from single-turn interactions to coordinated multiagent workflows, low-latency inference becomes increasingly important.

Foundation models are reshaping computational biology. Pretrained on massive corpora of protein or genomic sequences, models such as ESM2 (a protein language…

Developers building real-time AI—such as chat assistants, copilots, and agentic workflows—are often constrained by token-by-token generation speed.

As enterprise AI adoption scales, developers are increasingly forced to stitch together fragmented pipelines—separate models for text, vision…

Single-turn chatbots are evolving into long-running agents that can reason, maintain context, use tools, and run efficiently across many turns to complete…

Developing autonomous vehicle (AV) policies requires bridging an important gap between training and deployment. Vision-language-action (VLA) models that can…

Physical AI systems must understand the real world before they can act within it. Robots, autonomous vehicles, and smart spaces need to understand what’s…

AI applications are moving beyond text generation to multimodal systems that can perceive, search, and reason across images, documents, video…

Large language models (LLMs) are revolutionizing the financial trading landscape by enabling sophisticated analysis of vast amounts of unstructured data to…

This post is the second of a three-part series. See also Model Quantization: Concepts, Methods, and Why It Matters and Model Quantization: Turn FP8 Checkpoints…

Agentic systems often reason across screens, documents, audio, video, and text within a single perception‑to‑action loop. However, they still rely on fragmented…

DeepSeek just launched its fourth generation of flagship models with DeepSeek-V4-Pro and DeepSeek-V4-Flash, both targeted at enabling highly efficient million…

NVIDIA Ising is the world’s first family of open AI models for building quantum processors, launching with two model domains: Ising Calibration and Ising…

The release of MiniMax M2.7 adds enhancements to the popular MiniMax M2.5 model, built for agentic harnesses, and other complex use cases in fields such as…

In vision AI systems, model throughput continues to improve. The surrounding pipeline stages must keep pace, including decode, preprocessing, and GPU scheduling.

The Gemmaverse expands with the launch of the latest Gemma 4 multimodal and multilingual models, designed to scale across the full spectrum of deployments…

Developing new protein-based therapies and catalysts involves the challenging task of designing protein binders, or proteins that bind to a target protein or…

Agentic AI is an ecosystem where specialized models work together to handle planning, reasoning, retrieval, and safety guardrailing. As these systems scale…

Agentic AI systems need models with the specialized depth to solve dense technical problems autonomously. They must excel at reasoning, coding…

In the rapidly evolving landscape of large language model (LLM) development, NVIDIA Megatron Core has emerged as the foundational framework for training massive…

Alibaba has introduced the new open source Qwen3.5 series built for native multimodal agents. The first model in this series is a ~400B parameter native vision…

The article discusses the use of NVFP4 low-precision model training to achieve higher throughput without sacrificing accuracy in AI model training.

The article discusses how NVIDIA's hardware-software co-design significantly enhanced the inference performance of Sarvam AI's Sovereign 30B model, achieving a 4x speedup on NVIDIA Blackwell archit...

The article discusses NVIDIA TensorRT LLM AutoDeploy, a beta feature that automates the inference optimization process for large language models (LLMs).

Kimi K2. 5 is an advanced multimodal vision language model (VLM) developed by Kimi, optimized for various AI tasks.

The article provides a comprehensive guide on building a document processing pipeline using NVIDIA Nemotron RAG, focusing on the extraction of structured data from complex documents like PDFs.

The article discusses how to utilize NVIDIA Earth-2 to downscale coarse climate projections into high-resolution, bias-corrected fields, enabling better assessment of local climate extremes.

This article explores how to train an AI agent to operate a new Command Line Interface (CLI) using synthetic data generation and reinforcement learning.

The article discusses the development of generalist humanoid capabilities using NVIDIA Isaac GR00T N1. 6 through a sim-to-real workflow.

The article discusses the introduction of NVIDIA TensorRT Edge-LLM, an open-source C++ framework designed for high-performance inference of Large Language Models (LLMs) and Vision Language Models (...

The article discusses the latest software and model optimizations for NVIDIA DGX Spark, highlighting significant performance improvements in AI workflows.

The article discusses the NVIDIA Rubin platform, which introduces six new chips designed to create a powerful AI supercomputer.