NVIDIA NVLink Fusion Brings NVHBM to Next-Generation AI Infrastructure
AI factories must support increasingly large models and more complex reasoning workloads. To keep up with the insatiable compute demands of AI workloads,...
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194 条资讯按来源发布或更新时间排序
Intel OpenVINO 发布: 2026.3.1
### Summary of major features and improvements * Functionally enabled Muse-Glimmer-30B model: * Model card and IR available: [OpenVINO/Muse-Glimmer-30B-int4-ov · Hugging Face](https://huggingface.co/OpenVINO/Muse-Glimmer-30B-int4-ov) * Notebooks to try: * [Mus
vLLM 发布: v0.28.0
# v0.28.0 ## Highlights This release features 584 commits from 270 contributors (76 new)! * **Kimi-K3 performance push**: a major optimization effort for Kimi-K3 across the stack — Decode Context Parallel (DCP) support (#50484), fused FlashKDA decode and prefi
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 CPU: Olympus Cores Built for Maximum Single-Thread Performance in Agentic AI
Agentic AI shifts more of the critical execution path onto the CPU. Agents operate in sandboxes to execute code, invoke tools, retrieve context, interact with...
SGLang 发布: v0.5.18
# Highlights *710 PRs from 212 contributors.* **New models in this release** (see the [cookbook](https://docs.sglang.io/cookbook) for all supported models): | Model | Type | PRs | Cookbook | |---|---|---|---| | Muse Glimmer | Autoregressive (Multimodal) | [#34
ONNX Runtime 发布: ONNX Runtime WebGPU Plugin EP v0.3.0
ONNX Runtime WebGPU Plugin EP 0.3.0 expands model and data-type coverage, improves generative-model performance, and strengthens configuration, reliability, and release tooling. These release notes were drafted with AI assistance. ## Highlights ### Model and o
CUDA Python 1.0: Stable APIs, One Foundation, Full Platform Access
For years, a Python developer who needed a GPU had two realistic choices: Learn NVIDIA CUDA C++ well enough to write an extension, set up a build toolchain, and...
NVIDIA Vera Rubin and Blackwell Set a New Standard for Agentic AI Performance per Watt
AI agents have expanded inference from single-turn interactions into multi-step workflows that reason, invoke tools, coordinate subagents, and carry growing...
NVIDIA BlueField-4 Powers New Scale-In Network Infrastructure for Agentic AI Factories
Traditional cloud infrastructure was designed for predictable, general-purpose workloads and standard interfaces. Agentic AI factories connect diverse users,...
Solving Agentic AI Fleet Challenges with NVIDIA Vera CPU
AI factories are interconnected systems where fleet economics depend on how efficiently the entire stack converts power and capital into completed agent tasks....
How Generative Recommenders Are Redefining RecSys at Scale
Recommender systems (RecSys) are one of the most ubiquitous machine learning problems in the consumer internet industry yet notoriously difficult to train and...
GPU-Accelerated Clustering for Financial Instruments at Scale
Use AdaptGrow, a GPU-accelerated matrix factorization algorithm, to turn rolling correlation and tail-dependence matrices into hard clusters, soft factor...
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, moving...
Advancing Semiconductor Innovation Across Materials Engineering and Manufacturing
As AI workloads increase, explosive compute demand is pushing the semiconductor industry to meet unprecedented performance targets. Even small delays can have...
Developing Healthcare Robotics with GPU-Native Medical Physics Simulation
Unlike autonomous driving or industrial robotics, healthcare robotics can’t rely on internet-scale data collection or unlimited real-world experimentation....
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...
NVIDIA Exemplar Cloud: Lessons for Unlocking Full Performance on AI Infrastructure
Two AI computing clusters built from identical NVIDIA H100, GB200 NVL72, or GB300 NVL72 systems can deliver materially different training throughput. We...
Run High-Performance Core Math at Scale with NVIDIA nvmath-python
NVIDIA nvmath-python is a library designed to bridge the gap between the Python scientific community and NVIDIA CUDA-X math libraries. It gives Python users...
Co-Designing AI Model Attention for Fast, Interactive Long-Context Inference
As agentic and long-context workloads become common, the context lengths increase and attention consumes a larger share of inference time (Figure 1). Because...
How to Run Isolated Tenant Kubernetes Clusters on Shared GPU Infrastructure
Running a dedicated Kubernetes cluster per team often results in more isolation than an organization requires. While one cluster can be successfully shared...
NVIDIA Vera Storage Benchmarks: Faster Encryption, Compression, Integrity Checking, and Recovery for AI-Native Storage
Storage is an active part of every agentic AI workflow. As agents retrieve enterprise knowledge, access persistent memory, reuse key-value (KV) cache data,...
Generate Trajectories, Reasoning Traces, and Auto-Labels with NVIDIA Alpamayo 2 Super
Autonomous vehicle (AV) development often relies on separate models for trajectory generation, high-level intent prediction, scene understanding, and data...
Beyond VLAs: How World Action Models Reshape Robot Manipulation
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...
How to Choose Full-Stack Observability for NVIDIA AI Factories
AI infrastructure spans multiple layers, from compute and networking to storage, orchestration, and applications. When performance degrades, identifying the...
NVIDIA JetPack 7.2.1 Adds Agentic Video Skills and T3000 Emulation
Video is a core data path across NVIDIA Jetson applications, from robotics and intelligent video analytics to industrial automation, healthcare, media...
Run Massive-Scale UMAP in Minutes Using Multiple GPUs—Without Losing Accuracy
Uniform Manifold Approximation and Projection (UMAP) is a dimensionality reduction technique widely used for visualization and feature extraction. Applications...
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...
Post-Train NVIDIA Cosmos 3 Edge for On-Device Robot Control
Robots need policies that can adapt to their sensors, environments, and tasks while running on onboard computing hardware. World models offer a foundation for...
Building Federated Multimodal AI Workflows with NVIDIA FLARE
Modern vision-language models (VLMs) can support tasks such as visual question answering, captioning, and image-text reasoning. In practice, however, the data...