AICR v1.0: Open, stable, and verifiable GPU cluster configuration
GPU-accelerated Kubernetes clusters depend on compatible versions across dozens of components, each on its own release cycle: host kernels, GPU drivers,...
Deploying an HSTU Generative Recommender with NVIDIA Dynamo-Triton
Generative recommender (GR) systems are emerging as a powerful new approach for large-scale personalization. Instead of treating recommendation as a set of...
How NVIDIA DSX MaxLPS Maximizes AI Factory Throughput and Efficiency
Every unused watt is capacity left on the table. AI factories are typically provisioned for the unlikely moment when every GPU reaches peak power, creating a...
Simplifying Model Serving Across Multiple GPUs with NVIDIA TensorRT Multi-Device Integration in NVIDIA Dynamo-Triton
The compute and memory demands of generative AI increasingly exceed what a single GPU can provide. NVIDIA TensorRT multi-device inference is a new capability...
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...
Introducing CUDA Rust: Two Tracks for Writing GPU Kernels
In September 2026, NVIDIA announced it is leaning into native GPU programming in Rust. CUDA C++ and CUDA Python are mature, enterprise-grade toolchains, and...
Restore LLM Inference Capacity in Seconds with Shadow Engine Recovery in NVIDIA Dynamo
When an LLM engine process fails, the standard recovery path involves a cold restart. This requires loading weights into HBM from storage, compiling kernels,...
NVIDIA Vera Rubin and Blackwell Set a New Standard for Agentic AI Performance per WattÂ
AI agents have expanded inference from single-turn interactions into multi-step workflows that reason, invoke tools, coordinate subagents, and carry growing...
Maximizing AI Factory Performance per Watt with NVIDIA DSX MaxLPS
AI factories are power-constrained industrial systems. The question is no longer how many GPUs fit in a data center, but how much AI output each available...
Serve Qwen3.8-2.4T-A95B, a 2.4T-Parameter Model, with Configurable Reasoning on NVIDIA GB300 NVL72
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...
ModelExpress: Distributing Model Artifacts at the Speed of Light
Every byte moved has a cost. As model checkpoints grow to hundreds of gigabytes or even a terabyte, that cost adds up quickly. To make things even worse,...
NVIDIA NVLink: The Scale-Up Network for AI Factories
The demand for AI continues to accelerate. Workloads are getting larger, models are becoming more complex, and there is mounting pressure to deploy AI compute...
Hardware-Rooted AI Security That Won't Slow You Down
AI has transformed how organizations operate, driving unprecedented levels of productivity and innovation. However, AI adoption can be impeded by concerns...
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...
NVIDIA Achieves Leading Agentic Coding Performance on First Agentic AI Benchmark
AI agents have fundamentally changed the complexity of inference workloads. Until now, the industry has struggled to define a standard for measuring how...
Deploy Long-Context Reasoning and Agentic Workflows with MiniMax M3 on NVIDIA Accelerated Infrastructure
As enterprise AI adoption scales, developers are increasingly forced to stitch together fragmented pipelines—separate models for text, vision, and code—leading...
