Efficient MoE Training for Biological Foundation Models
As language models grow, scaling dense architectures becomes increasingly expensive. In a dense transformer, every token passes through every layer, so adding...
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1针对长上下文编程优化，Claude Opus 5.5 正式发布87 热度 ⌁2Cerebras推出CS-4晶圆级AI加速器：推理速度号称达GPU系统的30倍84 热度 ⌁3传OpenAI即日预览网络安全专用模型GPT-6 Cyber82 热度 ⌁4智能体编程加剧CI负载：Anthropic 如何扩展测试影响分析81 热度 ⌁5将“激活神谕”技术扩展至万亿参数模型：深入内部表征监测AI异常行为81 热度 ⌁9月24日2026-09-24
Introducing NV-Reason-CT Open 3D CT VLM for Radiologist Chain-of-Thought Reasoning
Radiology AI has made remarkable strides in detecting abnormalities across chest X-rays, pathology slides, and 2D scans. Yet one of the most clinically rich and...
Validate GPU Cluster Readiness Before AI Workloads Land
A GPU cluster can pass every health check and still fail to run an AI workload. Even when every GPU, network link, and pod reports healthy, a 512-GPU training...
Manage Kubernetes Node Fleets with NodeWright
Kubernetes manages what runs on your nodes. Managing the nodes themselves is the challenge: kernel settings, system packages, storage layouts, security agents,...
How SWE-Serve Exposes the Gap Between Local Tests and Live Serving
An AI coding agent’s patch can pass tests yet fail when the server loads a real model and handles requests. Evaluating changes to inference-serving software...
9月23日2026-09-23
Enabling Private High-Performance Production AI Inference with NVIDIA Confidential Computing
As large language model (LLM) inference increasingly processes sensitive information and proprietary model context across personal, enterprise, and regulated...
Topology-Aware Workload Scheduling with NVIDIA Topograph
AI factories are power-limited systems that deliver maximum value when fully optimized. GPU workload placement is a key optimization. Poor workload placement...
9月22日2026-09-22
What’s New for Game Developers: DLSS 5 with 3D-Guided Neural Rendering, NVIDIA ACE Updates, and New RTX Kit Capabilities
NVIDIA DLSS 5 introduces DLSS 3D-Guided Neural Rendering and granular controls that help game developers add lifelike lighting and material detail while...
Accelerating a ROS 2 Node with an AI Agent and NVIDIA Isaac ROS
GPU acceleration can speed up compute-intensive robotics workloads, but a fast CUDA kernel alone does not guarantee a fast ROS 2 graph. As messages move between...
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...
How to Evaluate AI Agents From Tool Calls to Task Completion
When you ship an AI agent, the key question is whether it can execute a chain of work across dozens of sequential tool calls against a live environment, and...
9月21日2026-09-21
Turn Your Latest Observations Into Timely Weather Decisions With NVIDIA Earth-2
Weather-sensitive industries increasingly have access to observations that offer an earlier, more local view of changing conditions. Energy companies collect...
9月19日2026-09-19
Benchmarking LLM Inference at Scale with AIPerf
You’re deploying a model on a system. It starts up, prompts are getting responses. Now the hard question: Is this fast? Your instincts might lead you to send...
9月17日2026-09-17
How to Use AI Agents to Prepare 3D Scenes for Simulation
Agentic AI workflows can be used to prepare and validate digital twins for physical AI systems. Agents can inspect 3D scenes, author simulation-relevant data in...
TensorRT Edge-LLM Completes the MLPerf Edge Agentic Benchmark 6.4x Faster on Jetson AGX Thor
AI agents are moving from cloud data centers to vehicles, robots, and other edge devices. Unlike a chatbot that answers a single prompt, an agent works through...
Translating CUDA Tile Operations from Python to Rust Using Agentic AI
cuTile Rust (cutile-rs) is a tile-based system for safe, idiomatic GPU kernel authoring in the Rust programming language. Extending the Rust ownership model to...
9月16日2026-09-16
Dense vs. MoE Models: Active Parameters, Throughput, and When to Choose Each
How can a 30B-parameter model activate only 3B parameters per token, and still use the capacity of the larger model? Nemotron 3.5 Lightning illustrates the...
How NVIDIA Groq 3 LPX Deterministic Execution Drives Power-Efficient High-Interactivity Inference on NVIDIA Vera Rubin
Power is a defining constraint for AI factories. As AI workloads demand a full compute platform to serve them, each component of that platform must maximize...
How NVIDIA NVLink 6 Delivers Multi-Layer Resiliency for AI Factories
For operators of large-scale AI factories, maximizing continuous output is essential for productivity. In massive-scale AI training, every GPU in the cluster...
9月15日2026-09-15
Scaling Federated Learning Across Docker, Kubernetes, and Slurm with NVIDIA FLARE
Federated learning (FL) projects often begin with a straightforward setup: one server, a few clients, and one dataset at each site. As those projects grow, the...
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...
9月11日2026-09-11
How Full-Stack NIM Optimizations Deliver 2.5x More Users on Nemotron 3 Ultra
Deploying a large language model is only the first step toward production-ready serving. Production teams also need to serve as many concurrent users as...
9月10日2026-09-10
High-Throughput Structure Prediction with BioNeMo Inference Runtime
Biomolecular structure prediction is now often run at proteome scale, where the goal is to move an entire worklist through the pipeline efficiently. NVIDIA...
From Wafer-Out to First Token: Codifying Supply Chain Expertise with Nemotron and Palantir Foundry
NVIDIA has one of the largest and most complex supply chains in the world, and its performance is measured from wafer-out to first token. The interval is in two...
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 prefill...
CUDA Toolkit 13.4 Adds Windows on Arm Support and Greater Control over Shared GPUs
Every NVIDIA CUDA Toolkit release adds functionality and performance improvements that help developers get more from NVIDIA GPUs and the broader NVIDIA software...
9月8日2026-09-08
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...
9月5日2026-09-05
Building a Memory-Driven Agent with NVIDIA NemoClaw
Enterprise work spans messages, decisions, projects, and obligations that change over time. An AI agent that starts without this context must reconstruct it...
Frontier Reasoning Reaches the Edge: How to Deploy and Optimize Models on NVIDIA Jetson
Running reasoning and agentic AI at the edge has been harder than it needs to be. Until recently, models capable of multi-step reasoning were too large to run...
9月4日2026-09-04
How to Carry User Identity Across Federated Kubernetes and AI Platforms
Modern AI platforms are no longer a single application behind one login screen. A user may start in a central portal, open a governed dataset, launch a notebook...
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