--- 格式版本: 2 标题: "OpenAI’s New GPT-5.5 Powers Codex on NVIDIA Infrastructure | NVIDIA Blog" 原文链接: "https://blogs.nvidia.com/blog/openai-codex-gpt-5-5-ai-agents/" 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: "该日期来自HTML metadata中的article-date字段，位于标题附近，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: 36 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: "2026-07-20T14:59:44+08:00" 发现时间: "2026-07-20T09:27:23+08:00" 入库时间: "2026-07-20T07:11:14.486Z" 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: 75 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机柜级系统支撑GPT-5.5及OpenAI 10GW部署计划，但正文侧重AI Agent应用与软件栈，缺乏超节点硬件架构、供电散热互连等技术细节，商业信号偏宏观。" AI质检模型: "qwen3.6-plus" AI质检时间: "2026-07-20T15:11:14+08:00" AI主题相关性: 15 AI来源权威性: 14 AI新颖性: 16 AI技术细节: 10 AI商业部署信号: 10 AI完整性: 10 图片摘要: - "✓ ./assets/img-724a76f8.jpg | infographic | 总结OpenAI与NVIDIA合作，GPT-5.5/Codex基于GB200 NVL72，强调低成本、高吞吐及10+GW算力承诺。" - "✓ ./assets/img-83d2f103.jpg | photo | 展示NVIDIA GB200 NVL72机架级系统部署，支撑GPT-5.5的高能效推理。" - "✗ ./assets/img-1f6575f1.png | diagram | 与正文主题（GPT-5.5/GB200）无关，涉及NVIDIA Vera CPU技术。" - "✗ ./assets/img-3511966c.jpg | photo | 品牌宣传/装饰性配图（NVIDIA总部），无具体技术信息。" - "✓ ./assets/img-e47e99dc.png | photo | 展示NVIDIA推理软件栈与硬件结合，实现最低Token成本。" 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-codex-gpt-5-5-ai-agents" --- AI agents have revolutionized developer workflows, and their next frontier is knowledge work: processing information, solving complex problems, coming up with new ideas and driving innovation. Codex, OpenAI’s agentic coding application, is enabling this new frontier. It’s now powered by GPT-5.5, OpenAI’s latest frontier model, which runs on NVIDIA GB200 NVL72 rack-scale systems. Over 10,000 NVIDIANs — across engineering, product, legal, marketing, finance, sales, HR, operations and developer programs — are already using GPT-5.5-powered Codex to achieve, in their words, “mind-blowing” and “life-changing” results. NVIDIA engineers have had access to GPT-5.5 through the Codex app for a few weeks, and the gains are measurable. Served on GB200 NVL72, which is capable of delivering 35x lower cost per million tokens and 50x higher token output per second per megawatt compared with prior-generation systems — [economics](https://blogs.nvidia.com/blog/lowest-token-cost-ai-factories/) that make frontier-model inference viable at enterprise scale. Debugging cycles that once stretched across days are closing in hours. Experimentation that previously required weeks is turning into overnight progress in complex, multi-file codebases. Teams are shipping end-to-end features from natural-language prompts, with stronger reliability and fewer wasted cycles than earlier models. OpenAI’s stunning progress is just the latest example of NVIDIA’s work with every frontier model company — not just to accelerate the use of AI agents inside NVIDIA, but to help the company’s partners build the world’s best, lowest cost and most power efficient models for everyone. As NVIDIA founder and CEO Jensen Huang told employees in a company-wide email urging everyone to use Codex: “Let’s jump to lightspeed. Welcome to the age of AI.” ## A Deployment Built for Enterprise Security Just like humans, every agent needs its own dedicated computer. To ensure seamless operation within secure enterprise environments, the Codex app supports remote Secure Shell (SSH) connections to approved cloud virtual machines, allowing agents to work with real company data without exposing it externally. So to ensure maximum security and auditability, NVIDIA IT rolled out cloud virtual machines (VMs) for every employee to run their agent safely. This provides a dedicated sandbox for the agent to operate at its maximum capabilities while maintaining full auditability. Users can control the Codex agent running in the cloud VM from a user interface that every employee is familiar with. A zero-data retention policy governs NVIDIA’s deployment, and agents access production systems with read-only permissions through command-line interfaces and Skills — the same agentic toolkit NVIDIA uses to run automation workflows across the company. ![图片](./assets/img-724a76f8.jpg) ## A Decade of Full-Stack Collaboration The GPT-5.5 launch and the Codex rollout reflect more than 10 years of collaboration between NVIDIA and OpenAI. The partnership began in 2016, when Huang hand-delivered the first NVIDIA DGX-1 AI supercomputer to OpenAI’s San Francisco headquarters. Since then, the two companies have worked closely across the full AI stack. NVIDIA was a day-zero partner for OpenAI’s gpt-oss open-weight model launch, optimizing model weights for NVIDIA TensorRT-LLM and ecosystem frameworks including vLLM and Ollama. OpenAI has committed to deploying more than 10 gigawatts of NVIDIA systems for its next-generation AI infrastructure — a buildout that will put millions of NVIDIA GPUs at the foundation of OpenAI’s model training and inference for years ahead. And OpenAI and NVIDIA are early silicon and codesign partners: OpenAI provides feedback that informs NVIDIA’s hardware roadmap, and in turn gains early access to new architectures. That relationship produced a concrete milestone — the joint bring-up of the first GB200 NVL72 100,000-GPU cluster. The cluster completed multiple large-scale training runs and set a new benchmark for system-level reliability at frontier scale. GPT-5.5 is the product of that infrastructure running at full strength. *Learn more in* [*OpenAI’s announcement*](https://openai.com/index/introducing-gpt-5-5/)*.* ![图片](./assets/img-724a76f8.jpg) ![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)