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...
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汇集 NVIDIA 显卡、GPU 算力、CUDA、AI 工作站与驱动更新。
164 条资讯按来源发布或更新时间排序
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...
Evaluating AI Agent Skill Performance with NVIDIA SkillEvaluator
AI agents are only as effective as the context they receive. Even with capable models and well-documented NVIDIA libraries, agents can spend extra steps finding...
Developing NVIDIA Holoscan Applications with CLI, Skills, and AI Coding Agents
NVIDIA Holoscan is a platform for building real-time AI applications at the edge, from medical imaging to robotics. HoloHub is its companion repository: a...
Transformers 发布: Patch release: v5.15.1
# Patch release v5.15.1 This patch most notably solves a few issues with DFlash and MTP candidate generators, as well as an issue where images could sometimes not be processed on accelerator if using Lanczos filter. It contains the following commits: - Fix DFl
ONNX Runtime 发布: ONNX Runtime CUDA Plugin EP 0.1.0
This is the first release of ONNX Runtime CUDA Plugin EP, providing CUDA execution as a separately packaged plugin execution provider. These notes cover commits affecting CUDA Plugin EP core code, CMake integration, and its primary build and package pipeline.
How NVIDIA scales expertise with ChatGPT Work
NVIDIA teams use ChatGPT Work to reduce manual tasks, connect fast-moving signals, and scale successful workflows globally.
ComfyUI 发布: v0.33.1
## What's Changed * Fix KSamplerAdvanced with add_noise disabled on nested latents by @kijai in https://github.com/Comfy-Org/ComfyUI/pull/15447 * Update workflow templates to v0.11.40 by @comfyui-wiki in https://github.com/Comfy-Org/ComfyUI/pull/15522 * chore:
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...
SGLang 发布: v0.5.17
# Highlights *582 PRs from 194 contributors.* **Kimi K3 day-0 support**: A 2.8T-parameter multimodal LatentMoE (896 experts, top-16, routed in a 3584-dim latent space) with a 1M-token context, 69 KDA linear-attention layers interleaved with 24 MLA layers, and
Integrating Context-Aware Video AI Agents Into Enterprise Workflows
A video analytics AI agent that can perceive, reason, and act based on massive amounts of video footage must be integrated with existing workflows and...
Q&A: How Capcom Brought Path Tracing to RE ENGINE Across PRAGMATA and Resident Evil Requiem
Capcom's RE ENGINE team set out to bring path tracing into two shipping titles at once, Resident Evil Requiem and PRAGMATA, each with a different visual...
Integrate NVIDIA Omniverse RTX Sensor Simulation Into Existing Apps
Developers building 3D, design, simulation, robotics, and industrial digital twin applications need ways to bring physical AI capabilities into the tools and...
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....
Make Long-Running NVIDIA TensorRT Engine Builds Observable and Cancelable in Python or C++
A TensorRT engine build can take seconds to many minutes. Large strongly typed models, deep tactic search, and a cold timing cache on a brand-new GPU SKU can...
Setting a World Record for MoE Pre-Training on NVIDIA GB300 NVL72
Frontier model pre-training has converged on mixture of experts (MoE), which is fundamentally changing what limits large-scale AI training. As compute per token...
Debugging Ray Tracing Applications Using NVIDIA OptiX Toolkit
NVIDIA OptiX ray tracing engine is an application framework for achieving optimal ray tracing performance on the GPU. Applications using OptiX can fail in ways...
Start Customizing NVIDIA Nemotron 3 Nano with Prime Intellect Lab in Minutes
Customization is what enables developers to take a general model and tailor it to use cases, domains, languages, and more. However, customization comes with a...
ONNX Runtime 发布: ONNX Runtime WebGPU Plugin EP v0.2.1
## Highlights - **Major performance work for attention-heavy LLMs.** - FlashAttention decode kernels were fused and extended for any sequence length ([#28389](https://github.com/microsoft/onnxruntime/pull/28389)). - FlashAttention prefill shared-memory path wa
vLLM 发布: v0.26.0
# vLLM v0.26.0 Release Notes ## Highlights This release features 411 commits from 212 contributors (61 new)! * **New Inkling model family** with a full support stack: base modeling (#48799), piecewise CUDA graph support (#48822), Hopper FA4 relative attention
ONNX Runtime 发布: ONNX Runtime v1.28.0
## Announcements & Breaking Changes - Upgraded to **ONNX 1.22.0** and protobuf 6.33.5 ([#28754](https://github.com/microsoft/onnxruntime/pull/28754), [#29606](https://github.com/microsoft/onnxruntime/pull/29606), [#28967](https://github.com/microsoft/onnxrunti
AT&T and Microsoft scale trillion-token workloads with Microsoft Foundry and AMD
AT&T processed approximately one trillion tokens while developing OTel2.0 using Microsoft Foundry Managed Compute, open AI models, and AMD and NVIDIA GPU infrastructure. Discover how flexible model choice and scalable infrastructure are enabling production-sca
PyTorch 发布: PyTorch 2.13.0 Release
# PyTorch 2.13.0 Release Notes - [Highlights](#highlights) - [Backwards Incompatible Changes](#backwards-incompatible-changes) - [Deprecations](#deprecations) - [New Features](#new-features) - [Improvements](#improvements) - [Bug fixes](#bug-fixes) - [Performa
SGLang 发布: v0.5.15.post1
v0.5.15.post1 includes a few patches, mostly for GLM 5.2 - #30454 #30627: Fix DSA model launching on non Cuda/HIP devices - #30858: Fix flashinfer dependency on Cuda 12 images - #31001: Fix NaN outputs caused by flashinfer trtllm FP4 MoE kernels on long input
vLLM 发布: v0.25.0
# vLLM v0.25.0 Release Notes ## Highlights This release features 558 commits from 232 contributors (64 new)! * **Model Runner V2 is now the default for all dense models** (#44443). Building on quantized-model support from the previous release, MRv2 is now the
ONNX Runtime 发布: ONNX Runtime v1.27.1
This is a patch release on top of [v1.27.0](https://github.com/microsoft/onnxruntime/releases/tag/v1.27.0), containing targeted bug fixes, a CUDA QMoE decode-path optimization, and CI/build infrastructure fixes. ## Bug Fixes - [MLAS] Fixed an `igemm` regressio
SGLang 发布: v0.5.15
# Highlights **GLM-5.2 NVFP4, tuned for production**: We took time this cycle to tune GLM-5.2 NVFP4 on Blackwell for optimized production serving. It now runs at **500+ tok/s/user on 8x B300, 450 on 4x GB300** (bs=1). Run GLM-5.2 with our [cookbook](https://do
Triton 编译器发布: Triton 3.7.0 Release Notes
## Table of Contents - [Dialect & Frontend](#dialect--frontend) - [Backend & Compiler](#backend--compiler) - [AMD/HIP Backend](#amdhip-backend) - [NVIDIA Backend](#nvidia-backend) - [Gluon & Layout Improvements](#gluon--layout-improvements) - [Kernels & Benchm