OpenAI发布了其首款由博通(Broadcom)制造的定制芯片。
OpenAI unveils its first custom chip, built by Broadcom

原始链接: https://techcrunch.com/2026/06/24/openai-unveils-its-first-custom-chip-built-by-broadcom/

OpenAI 发布了其首款与博通(Broadcom)合作研发的定制推理处理器“Jalapeño”。该芯片专为更高效地运行预训练人工智能模型而设计,旨在减少公司对英伟达昂贵 GPU 的依赖。早期测试表明其具备更出色的单位功耗性能,OpenAI 预计这将显著降低实时人工智能任务(如编程助手)的运营成本。 此举标志着 OpenAI 向垂直整合迈出了战略性的一步。通过设计涵盖芯片架构、内存系统和网络连接的自有基础设施,OpenAI 旨在优化其技术栈的每一层。虽然模型预训练等资源密集型任务目前可能仍会继续使用英伟达的硬件,但“Jalapeño”的研发凸显了该公司致力于使其产品更快速、更可靠且更具性价比的决心。最终,掌控底层芯片设计使 OpenAI 能够根据其独特且不断演进的人工智能模型定制硬件,从而在竞争激烈的人工智能部署领域中获得显著的经济优势。

OpenAI 宣布推出其与博通(Broadcom)合作研发的首款定制推理芯片“Jalapeño”。该芯片计划于 2026 年底投入使用,旨在提升运行 AI 模型时的单位功耗性能。 这一消息在 Hacker News 上引发了激烈讨论。持怀疑态度的人质疑 OpenAI 关于“利用其模型加速芯片设计过程”的说法,认为这可能只是“营销噱头”或常规的 AI 辅助编程,而非芯片架构上的革命性突破。另一些人则指出,谷歌和亚马逊等超大规模云厂商长期以来一直与博通合作,由后者负责台积电(TSMC)的产能管理并提供关键的知识产权模块。 讨论还涉及了行业向专用推理硬件转变的趋势。虽然一些用户强调了为极端延迟增益而设计的“固化”硅片模型(如 Taalas)的潜力,但另一些人指出,将硬件锁定在特定模型版本上存在固有局限。此外,人们还对博通在收购后大举削减成本的过往声誉,以及涉及 Cerebras 等参与者的竞争格局表示担忧。归根结底,该项目标志着 OpenAI 正转型为一家像同行一样对其基础设施进行垂直整合的公司,以减少对英伟达等外部供应商的长期依赖。
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原文

On Wednesday, OpenAI unveiled its first custom-built inference processor, designed and manufactured in collaboration with Broadcom. Named Jalapeño, the new processor was designed specifically for the unique needs of OpenAI’s inference systems. OpenAI’s own AI models assisted in the development of the chip, the company said.

While the chip is still being tested, OpenAI says early results show significantly better performance-per-watt than current state-of-the-art alternatives.

The partnership was officially announced in October, but OpenAI’s chip plans have long been rumored as a way to reduce the company’s dependence on Nvidia’s GPUs. Google and Amazon have both built custom chips to serve a similar purpose, often called “AI accelerators” — silicon designed specifically to speed up machine learning workloads.

OpenAI president Greg Brockman explained the company’s approach to chip development on its in-house podcast, shortly after the Broadcom partnership was announced.

“We have a deep understanding of the workload,” Brockman said in the episode. “We’ve really been looking for specific workloads that are underserved, [and asking] how can we build something that will be able to accelerate what’s possible?”

Jalapeño is specifically designed for inference, the process of running pre-built AI models in response to user commands. In the announcement, OpenAI emphasized the chip’s low operating cost when running real-time coding models. It’s likely that more performance-intensive tasks like pre-training will still rely on Nvidia hardware, but even small reductions in inference costs could do a lot to improve the company’s bottom line.

Optimizing that inference system may prove to be a crucial factor in the economics of AI going forward — and it’s likely to take place at every level of the stack. OpenAI is already building agentic products like Codex and the models that power them, as well as data centers to run those models. Moving into purpose-built chips lets the company go even further in that process, as the company explained in its announcement.

“OpenAI is not only developing frontier models or building products on top of them; it is designing the infrastructure underneath them: chip architecture, kernels, memory systems, networking, scheduling, deployment systems, and product experience,” the company wrote. “Because OpenAI operates across the stack, each layer can be optimized around the same goal: making its models faster, more reliable, and more affordable for users.”

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