--- 格式版本: 2 标题: "OpenAI and NVIDIA Propel AI Innovation With New Open Models Optimized for the World’s Largest AI Inference Infrastructure | NVIDIA Blog" 原文链接: "https://blogs.nvidia.com/blog/openai-gpt-oss/" 发布日期: "2026-06-30" 发布时间校准状态: "found" 发布时间来源: "llm:strict_original_body" 发布时间证据: "time class=related-news-date nvidia-article-date datetime=2026-06-30T08:00:57-07:00: Jun 30, 2026" 发布时间校准原因: "该日期位于标题下方，且带有 nvidia-article-date 类名，符合文章发布时间的特征。" 发布时间校准置信度: "100" 发布时间候选数量: 32 发布时间严格候选数量: 12 发布时间原页读取状态: "原页面来自已抓取 HTML" 发布时间未找到原因: "候选日期无效或 LLM 未确认" 发布时间校准时间: "2026-07-20T15:08:22+08:00" 发现时间: "2026-07-20T09:27:23+08:00" 入库时间: "2026-07-20T07:19:05.085Z" 来源平台: "NVIDIA Blog 搜索" 搜索渠道: "source_template" 搜索词: "https://blogs.nvidia.com/?s=OpenAI" 匹配关键词: - "GPU" - "NVL72" 相关厂家: - "OpenAI" - "NVIDIA" 相关专家: [] 内容类型: "网页" 抓取工具: "AgentKey Scrape" 清洗工具: "AgentKey Markdown + LLM 正文裁剪" 原始附件: [] AI优质: "否" AI打分: 68 AI分档: "召回候选" AI质检状态: "不通过" AI打分理由: "NVIDIA官方发布，提及GB200 NVL72及推理性能指标，但核心聚焦OpenAI开源模型与软件生态，缺乏超节点/AI Rack架构、供电散热互连等硬件细节，商业部署信息有限。" AI质检模型: "qwen3.6-plus" AI质检时间: "2026-07-20T15:19:05+08:00" AI主题相关性: 12 AI来源权威性: 14 AI新颖性: 16 AI技术细节: 8 AI商业部署信号: 8 AI完整性: 10 图片摘要: - "✓ ./assets/img-83d2f103.jpg | diagram | 展示GB200 NVL72等大规模AI推理基础设施集群的3D渲染图，体现硬件部署规模。" - "✗ ./assets/img-1f6575f1.png | diagram | 文章主要讨论OpenAI模型与GPU优化，未提及Vera CPU，属无关配图" - "✗ ./assets/img-3511966c.jpg | photo | NVIDIA品牌宣传照，无具体技术信息" - "✓ ./assets/img-e47e99dc.png | diagram | 展示NVIDIA推理软件栈（NIM）与Blackwell硬件结合的抽象示意图，体现软件优化。" 采集批次: "2026年7月20日9点23分34秒" 采集批次ID: "20260720-092334-062" 去重键: "https://blogs.nvidia.com/blog/openai-gpt-oss" --- Two new open-weight AI reasoning models from OpenAI released today bring cutting-edge AI development directly into the hands of developers, enthusiasts, enterprises, startups and governments everywhere — across every industry and at every scale. NVIDIA’s collaboration with OpenAI on these open models — [gpt-oss-120b](https://build.nvidia.com/openai/gpt-oss-120b) and [gpt-oss-20b](https://build.nvidia.com/openai/gpt-oss-20b) — is a testament to the power of community-driven innovation and highlights NVIDIA’s foundational role in making AI accessible worldwide. Anyone can use the models to develop breakthrough applications in [generative](https://www.nvidia.com/en-us/glossary/generative-ai/), [reasoning](https://www.nvidia.com/en-us/glossary/ai-reasoning/) and [physical AI](https://www.nvidia.com/en-us/glossary/generative-physical-ai/), healthcare and manufacturing — or even unlock new industries as the next industrial revolution driven by AI continues to unfold. OpenAI’s new flexible, open-weight text-reasoning large language models ([LLMs](https://www.nvidia.com/en-us/glossary/large-language-models/)) were trained on [NVIDIA H100 GPUs](https://www.nvidia.com/en-us/data-center/h100/) and [run inference best](https://www.nvidia.com/en-us/solutions/ai/inference/) on the hundreds of millions of GPUs running the [NVIDIA CUDA](https://developer.nvidia.com/cuda-toolkit) platform across the globe. The models are now available as [NVIDIA NIM microservices](https://build.nvidia.com/search?q=gpt-oss), offering easy deployment on any GPU-accelerated infrastructure with flexibility, data privacy and enterprise-grade security. With software optimizations for the [NVIDIA Blackwell](https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/) platform, the models offer optimal inference on [NVIDIA GB200 NVL72](https://www.nvidia.com/en-us/data-center/gb200-nvl72/) systems, achieving 1.5 million tokens per second — driving massive efficiency for inference. “OpenAI showed the world what could be built on NVIDIA AI — and now they’re advancing innovation in open-source software,” said Jensen Huang, founder and CEO of NVIDIA. “The gpt-oss models let developers everywhere build on that state-of-the-art open-source foundation, strengthening U.S. technology leadership in AI — all on the world’s largest AI compute infrastructure.” ## NVIDIA Blackwell Delivers Advanced Reasoning As advanced reasoning models like gpt-oss generate exponentially more tokens, the demand on compute infrastructure increases dramatically. Meeting this demand calls for purpose-built AI factories powered by NVIDIA Blackwell, an architecture designed to deliver the scale, efficiency and return on investment required to run inference at the highest level. NVIDIA Blackwell includes innovations such as [NVFP4](https://developer.nvidia.com/blog/introducing-nvfp4-for-efficient-and-accurate-low-precision-inference/) 4-bit precision, which enables ultra-efficient, high-accuracy inference while significantly reducing power and memory requirements. This makes it possible to deploy trillion-parameter LLMs in real time, which can unlock billions of dollars in value for organizations. ## Open Development for Millions of AI Builders Worldwide NVIDIA CUDA is the world’s most widely available computing infrastructure, letting users deploy and run AI models anywhere, from the powerful [NVIDIA DGX Cloud](https://www.nvidia.com/en-us/data-center/dgx-cloud/) platform to [NVIDIA GeForce RTX](https://www.nvidia.com/en-us/geforce/rtx/) – and [NVIDIA RTX PRO](https://www.nvidia.com/en-us/products/workstations/) -powered PCs and workstations. There are over 450 million NVIDIA CUDA downloads to date, and starting today, the massive community of CUDA developers gains access to these latest models, optimized to run on the NVIDIA technology stack they already use. Demonstrating their commitment to open-sourcing software, OpenAI and NVIDIA have collaborated with top open framework providers to provide model optimizations for FlashInfer, Hugging Face, llama.cpp, Ollama and vLLM, in addition to [NVIDIA Tensor-RT LLM](https://github.com/NVIDIA/TensorRT-LLM) and other libraries, so developers can build with their framework of choice. ## A History of Collaboration, Building on Open Source Today’s model releases underscore how NVIDIA’s full-stack approach helps bring the world’s most ambitious AI projects to the broadest user base possible. It’s a story that goes back to the earliest days of NVIDIA’s collaboration with OpenAI, which began in 2016 when Huang hand-delivered the first NVIDIA DGX-1 AI supercomputer to OpenAI’s headquarters in San Francisco. Since then, the companies have been working together to push the boundaries of what’s possible with AI, providing the core technologies and expertise needed for massive-scale training runs. And by optimizing OpenAI’s gpt-oss models for NVIDIA Blackwell and RTX GPUs, along with NVIDIA’s extensive software stack, NVIDIA is enabling faster, more cost-effective AI advancements for its 6.5 million developers across 250 countries using 900+ NVIDIA software development kits and AI models — and counting. *Learn more by reading the [NVIDIA Technical Blog](https://developer.nvidia.com/blog/delivering-1-5-m-tps-inference-on-nvidia-gb200-nvl72-nvidia-accelerates-openai-gpt-oss-models-from-cloud-to-edge/) and* [*latest installment of the NVIDIA RTX AI Garage blog series*](https://blogs.nvidia.com/blog/rtx-ai-garage-openai-oss)*.* *Get started building with the [gpt-oss models](https://build.nvidia.com/search?q=gpt-oss).* ![Why Performance per Watt Is the Ultimate Metric for AI Infrastructure Efficiency](./assets/img-83d2f103.jpg) ![How NVIDIA’s Inference Software Stack Powers the Lowest Token Cost](./assets/img-e47e99dc.png)