Data Center / Cloud

AI Factories, Physical AI, and Advances in Models, Agents, and Infrastructure That Shaped 2025

Four-image grid illustrating AI agents, robotics, data center infrastructure, and simulated environments.

2025 was another milestone year for developers and researchers working with NVIDIA technologies. Progress in data center power and compute design, AI infrastructure, model optimization, open models, AI agents, and physical AI redefined how intelligent systems are trained, deployed, and moved into the real world. These posts highlight the innovations that resonated most with our readers.

Render of the 800V DC power architecture.

NVIDIA 800V HVDC Architecture Will Power the Next Generation of AI Factories

As AI racks move to megawatt scale, NVIDIA and industry partners are advancing an 800V DC power architecture to deliver higher efficiency, scalability, and reliability for future data centers.

Newton robot walking.

Announcing Newton: An Open-Source Physics Engine for Robotics Simulation

Newton, developed jointly by NVIDIA, Google DeepMind, and Disney Research, provides an open, customizable physics engine built on NVIDIA Warp for accurate, scalable robotic simulation and learning.

A render of a palace.

NVIDIA RTX Neural Rendering Introduces Next Era of AI-Powered Graphics Innovation

The NVIDIA GeForce RTX 50 Series GPUs launch with an RTX Kit, a set of neural rendering technologies for developers to integrate AI-enhanced geometry, textures, materials, and lighting into their rendering pipelines.

A render of the NVIDIA Blackwell.

Introducing NVFP4 for Efficient and Accurate Low-Precision Inference

The fifth-generation NVIDIA Blackwell Tensor Cores add support for multiple 4-bit floating-point formats, including NVFP4, to improve quantization efficiency while maintaining task-specific accuracy.

An image representing GPU attention kernels.

Automating GPU Kernel Generation with DeepSeek-R1 and Inference-Time Scaling

NVIDIA engineers use the DeepSeek-R1 model with inference-time scaling to automatically generate optimized, numerically correct GPU attention kernels—showing how AI can accelerate or even surpass traditional hand-tuned kernel development.

A decorative image.

Introducing NVIDIA Dynamo: A Low-Latency Distributed Inference Framework for Scaling Reasoning AI Models

NVIDIA Dynamo is a high-throughput, low-latency inference framework that boosts DeepSeek-R1 performance by up to 30x on NVIDIA Blackwell GPUs and introduces disaggregated serving, dynamic scheduling, and LLM-aware routing for scalable generative AI deployment.

A render of the NVIDIA Blackwell.

NVIDIA Blackwell Delivers World-Record DeepSeek-R1 Inference Performance

Enhanced compute, interconnect, and software optimizations deliver major DeepSeek-R1 throughput gains, surpassing 250 tokens per second per user.

An image showing use case such as robotics and healthcare for the NVIDIA Jetson platforms.

Introducing NVIDIA Jetson Thor: The Ultimate Platform for Physical AI

Generalist robotics is emerging as robots shift from fixed-function machines to adaptable systems powered by foundation models, with new NVIDIA Jetson platforms enabling multimodal reasoning and flexible task performance.

Decorative image.

Building the 800 VDC Ecosystem for Efficient, Scalable AI Factories

AI is reshaping data centers into power-driven AI factories, making an 800V DC architecture with integrated energy storage essential for scaling modern workloads efficiently.

Blackwell Ultra illustration.

Inside NVIDIA Blackwell Ultra: The Chip Powering the AI Factory Era

NVIDIA Blackwell Ultra combines advanced silicon features and deeper system-level integration—including a dual-reticle design, high-bandwidth HBM3E, new Tensor Cores, and NVFP4—to increase performance and efficiency for large-scale AI training and reasoning.

Looking ahead

Stay tuned for more transformative innovations in 2026.

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