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科学家发现宇宙黎明时期“黑洞星”_我的网站
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The instance is designed to handle inference for mixture-of-experts models with up to 10 trillion parameters, the company said in a statement sent to the Global Times on Wednesday.
This marks the first supernode-form computing architecture in China to successfully run large language models exceeding 2 trillion parameters, according to the company.
Industry expert Tian Feng told the Global Times that the commercial rollout of supernode infrastructure could significantly reduce training cycles, lower costs, and speed up iteration for AI developers requiring massive computational resources.
The company said the new instance has already been used to power commercial services for large language models such as KimiK3 and Qwen3.8Max.
The Lingjun Zhenwu M890 supernode instance supports FP8/FP4 low-precision computing. Through the ICNSwitch 1.0 chip, its scale-up interconnect scale has been expanded from 16 cards to 64 cards, with inter-card interconnect bandwidth boosted to 800 GB/s. Enterprises can provision 64-card, high-speed-interconnect computing units through the cloud without building their own data centers, according to the company.
In training scenarios such as autonomous driving and embodied intelligence, the instance delivers three times the training performance compared with the previous-generation Zhenwu 810E, the company said.
Ulanqab, where the supernode instance debuted, is one of Alibaba Cloud's five super data centers. The facility sources approximately 90 percent of its electricity from green energy, providing a low-carbon operating environment for high-density computing power.
Leveraging its climate, energy and network advantages, Ulanqab has transformed from "China's potato hometown" into the "token factory" - a term increasingly used in the AI industry to describe infrastructure dedicated to producing the digital building blocks generated by large language models.
By the end of 2025, the city had attracted 84 data center projects, including 81 intelligent computing centers, with total investment exceeding 500 billion yuan ($74.1 billion) and operational computing power reaching approximately 172,000 petaflops, ranking it firmly in the nation's top tier, according to domestic media reports.
On August 6, China's largest AI computing industrial park was completed and put into operation in Ulanqab. The project highlights a broader race in China to build massive AI data centers capable of supporting the next generation of AI models while addressing soaring electricity demand, according to Chinese experts.
In recent years, Inner Mongolia has been rapidly positioning itself as a global-scale AI computing center cluster. Major technology companies, including Huawei, Tencent, ByteDance and Alibaba; telecom operators China Mobile, China Telecom and China Unicom; as well as cyberspace infrastructure service provider VNET, have established computing facilities in the region.
As the AI industry gradually transitions from the training era to the inference era and large model parameters continue to expand, supernodes have become a central battleground for AI infrastructure.
Chinese vendors are accelerating deployments in this space. Huawei has commercially deployed more than 750 sets of its Ascend 384 supernodes across industries including internet, telecom operators, finance, education, healthcare, transportation and manufacturing. It is also the only domestic supernode to have trained state-of-the-art (SOTA) models.
Baidu AI Cloud has also launched its Tianchi 256-card supernode based on Kunlun chips, with support for major models including Wenxin, DeepSeek, GLM, and MiniMax.
Meanwhile, supercomputer manufacturer Sugon has unveiled China's first fully domestic 100,000-card AI supercluster Sugon 8000 (Dengfeng), integrating supercomputing and AI computing on a unified architecture. It has now been connected to the national supercomputing internet to provide computing services to government, research, and enterprise clients nationwide.
Tian, former dean of SenseTime's Intelligence Industry Research Institute, told the Global Times that the flurry of domestic supernode launches reflects a broader inflection point as China's AI sector pivots from training capacity toward efficient, large-scale inference.
The expert further said that the commercial viability of these systems - evidenced by Huawei's extensive deployed base, Baidu's rapid model adaptation and Sugon's integration into the national computing network - suggests domestic vendors are moving beyond proof-of-concept to genuine production-grade infrastructure, a prerequisite for sustaining the next wave of trillion-parameter model proliferation, Tian noted.
The move also underscores China's push for self-reliance in AI infrastructure as US chip export restrictions continue to tighten, Tian said, noting that, in the supernode domain, Chinese companies are shifting from imported graphics processing units toward homegrown interconnect chips and domestic compute clusters, a transition that could reshape value allocation across the AI industry chain.
。 据最新一期《自然》杂志报道，美国麻省理工学院、奥地利科学技术研究院等机构研究人员利用詹姆斯·韦布空间望远镜（JWST），在宇宙诞生后约6.6亿年的宇宙黎明时期发现了迄今已知最早的“黑洞星”。这一由快速成长黑洞及致密气体构成的奇特天体，或有助于揭开神秘“小红点”的真实身份，并为解释早期宇宙超大质量黑洞的快速形成提供线索。 超大质量黑洞通常潜伏在大质量星系中心，质量可达太阳的数百万至数十亿倍。
B | 然而，科学家一直难以解释，为何在宇宙诞生后仅数亿年，就已经出现了一些质量巨大的黑洞。 此次发现的天体被命名为“MoM-BH*-1”。研究人员原本利用JWST寻找宇宙早期星系，却在观测数据中发现一个异常明亮、颜色偏红的光点。它的尺度只有太阳系大小，但能量输出却约为普通恒星产生的能量上限的1000亿倍，远超普通恒星核聚变所能达到的水平。 与此同时，MoM-BH*-1的光谱还呈现出一种异常强烈的“巴耳末跳跃”，这种光谱特征通常与恒星大气中的致密气体有关。同时，其光谱中几乎没有金属元素的明显痕迹，主要表现为氢和氦的特征。 研究人员模拟发现，极其致密的氢气可形成类似“巨大恒星表面”的气体包层，吸收特定波长的光，从而解释其特殊光谱。
C | 但这仍无法解释它惊人的亮度。进一步分析显示，最符合观测结果的情景是：MoM-BH*-1中心有一个约为太阳质量10万倍的黑洞，周围包裹着近似太阳系大小的致密氢气。黑洞吸积物质释放的能量为整个系统提供动力，外围气体则让它看起来像一颗巨大的恒星——研究人员因此将其称为“黑洞星”。 “黑洞星”的发现还有助于解释JWST近年来频繁发现的神秘“小红点”。
D | 这些紧凑、明亮且偏红的天体在早期宇宙中似乎随处可见，却在今天的宇宙中消失殆尽。研究人员发现，如果将类似MoM-BH*-1的天体置于早期星系中，其整体光谱可能与“小红点”高度相似。（记者张佳欣）。
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