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Sen. Laphonza Butler not running in 2024 after filling Dianne Feinstein's seat_我的网站

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A | California Sen. Laphonza Butler will not seek to be elected to a full term in 2024 after she was recently appointed to succeed the late Dianne Feinstein, a decision that removes one potential candidate from an already crowded field, she said in a statement on Thursday."I've spent the past 16 days pursuing my clarity - what kind of life I want to have, what kind of service I want to offer and what kind of voice I want to bring forward," she said. "After considering those questions, I've decided not to run for Senate in the upcoming election."Butler, a longtime Democratic strategist and labor leader in California who has never held elected office before, added that "knowing you can win a campaign doesn't always mean you should run a campaign" and said that while the decision may have caught some by surprise, "it's the right one for me."The news was first reported by The New York Times.Butler's appointment helped California Gov. Gavin Newsom fulfill his promise to appoint a Black woman to Feinstein's seat in the event of a vacancy. When Butler was sworn in on Oct. 3, she became only the third Black woman to ever serve in the upper chamber. She took her oath from Vice President Kamala Harris, who was the second.Formerly the president of EMILY's List, a pro-abortion access political group, Butler is also only the second openly gay woman to serve in the Senate after Tammy Baldwin was elected in 2012 in Wisconsin.With Butler's decision to step aside from the 2024 race, she clears the way for an open race to succeed Feinstein. In California, all candidates will run in the March primary together, regardless of party, with the top two advancing to the general election next November.The primary field currently features three high-profile congressional Democrats: Reps. Adam Schiff, Katie Porter and Barbara Lee.Last week, 10-time MLB All-Star and former Los Angeles Dodgers first baseman Steve Garvey announced that he was running for the seat as a Republican and, on Wednesday, Christina Pascucci, a former TV anchor and reporter in Los Angeles, said that she would also run as a Democrat.Schiff and Porter have so far led the field, according to 538.Butler, who will serve out the remainder of Feinstein's term, through January 2025, has not endorsed any of the candidates."California voters want leaders who think about them and the issues they care most about. I now have 383 days to serve the people of California with every ounce of energy and effort that I have," Butler said in her statement on Thursday. "Muhammad Ali once said, 'Don't count the days, make the days count.' I intend to do just that."。

Alibaba Cloud's Lingjun Zhenwu M890 supernode instance Photo: Courtesy of Alibaba Cloud

Lingjun Zhenwu M890 supernode instance Photo: Courtesy of Alibaba CloudAlibaba Cloud on Tuesday officially launched its Lingjun Zhenwu M890 supernode instance in Ulanqab, North China's Inner Mongolia Autonomous Region, with the first batch of instances now available for sale in the region.
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.
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