NVFP4

Sep 14, 2026

Accelerating Dropless MoE Training in JAX with NVIDIA Transformer Engine

Mixture of experts (MoE) has become one of the defining architectural trends in large-scale AI model training. DeepSeek, Qwen, and Mixtral are examples of MoE...
12 MIN READ
Sep 09, 2026

When to Use Encode-Prefill-Decode Disaggregation to Accelerate Multimodal Model Serving

Encode-prefill-decode (EPD) disaggregation is an inference optimization technique for multimodal models that separates the vision encoder stage from the...
14 MIN READ
Sep 02, 2026

Co-Designing AI Models Using Speculative Decoding for Faster LLM Inference

This post is the third in a series on AI model co-design. It explores how to accelerate LLM inference while maintaining accuracy using speculative decoding and...
15 MIN READ
Aug 17, 2026

Developing Nemotron 3.5 Lightning NVFP4 with QAD Using NVIDIA Model Optimizer

Teams customize their models to hit their targets for latency, speed, memory, and compute. With the open NVIDIA Nemotron family of models, developers can find...
18 MIN READ
Aug 11, 2026

NVIDIA Nemotron 3.5 Lightning Delivers Fast, Accurate Specialized Task Execution for Long-Running Agents

Long-running AI agents spend most of their time on high-volume execution: tool calls, result validation, and subagent delegation. Using a frontier reasoning...
8 MIN READ
Jul 27, 2026

NVIDIA Ising Enables Fully Automated Quantum Computer Calibration with Enhanced In-Context Learning

NVIDIA Ising Calibration is an open source vision language model (VLM) designed to interpret diagnostic outputs from quantum processors and determine how they...
4 MIN READ
Jul 21, 2026

Inside NVIDIA Rubin GPU Architecture: Powering the Era of Agentic AI

What began as discrete AI model training and human-facing chat interfaces has evolved into always-on AI factories dedicated to producing intelligence at scale....
15 MIN READ
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Jun 26, 2026

Creating the NVIDIA Nemotron 3 Ultra NVFP4 Checkpoint with NVIDIA Model Optimizer

As context windows grow longer, moving large model weights efficiently becomes critical to performance. A common way to address this is quantization, an...
16 MIN READ
Jun 23, 2026

Maximize AI Factory Energy Efficiency Through Full-Stack Inference and Training Optimizations

Power can account for 40% of the operating expenses (OpEx) to run an AI factory. Each watt can be spent on overhead, data ingestion, training, or generating...
10 MIN READ
Jun 16, 2026

How to Optimize Transformer-Based Models for Low-Precision Training

Transformer architectures are the backbone of many modern large language and generative AI models. As these models grow in size, training runs consume more GPU...
9 MIN READ
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Jun 08, 2026

Train Models Faster with JAX and MaxText Using NVFP4 on NVIDIA Blackwell

Pre-training frontier LLMs comes down to throughput. When training spans trillions of tokens across thousands of accelerators, every percentage point of step...
7 MIN READ
May 27, 2026

NVIDIA Blackwell Sets STAC-AI Record for LLM Inference in Finance

Large language models (LLMs) are revolutionizing the financial trading landscape by enabling sophisticated analysis of vast amounts of unstructured data to...
10 MIN READ
Mar 25, 2026

Scaling Token Factory Revenue and AI Efficiency by Maximizing Performance per Watt

In the AI era, power is the ultimate constraint, and every AI factory operates within a hard limit. This makes performance per watt—the rate at which power is...
10 MIN READ
Mar 24, 2026

Building NVIDIA Nemotron 3 Agents for Reasoning, Multimodal RAG, Voice, and Safety

Agentic AI is an ecosystem where specialized models work together to handle planning, reasoning, retrieval, and safety guardrailing. As these systems scale,...
10 MIN READ
Mar 11, 2026

Introducing Nemotron 3 Super: An Open Hybrid Mamba-Transformer MoE for Agentic Reasoning

Agentic AI systems need models with the specialized depth to solve dense technical problems autonomously. They must excel at reasoning, coding, and...
12 MIN READ
Feb 23, 2026

Using NVFP4 Low-Precision Model Training for Higher Throughput Without Losing Accuracy

As the sizes of AI models and datasets continue to increase, relying only on higher-precision BF16 training is no longer sufficient. Key challenges such as...
8 MIN READ