At Amazon’s annual cloud conference in 2016, the company captured the crowd’s attention by driving an 18-wheeler onstage. Andy Jassy, now Amazon’s CEO, called it the Snowmobile, and said the company would be using the truck to help customers speedily transfer data to Amazon Web Services facilities.
Less than eight years later, the semi is out of commission.
As of March, AWS had removed Snowmobile from its website, and the Amazon unit has stopped offering the service, CNBC has confirmed. The webpage devoted to AWS’ “Snow family” of products now directs users to its other data transport services, including the Snowball Edge, a 50-pound suitcase-sized device that can be equipped with fast solid-state drives, and the smaller Snowcone.
An AWS spokesperson said in an emailed statement that the company has introduced more cost-effective options for moving data. Clients had to deal with power, cooling, networking, parking and security when they used the Snowmobile service, the spokesperson said.
“Since we introduced Snowmobile in 2016, we’ve released many other new services and features which have made migrating data to AWS even faster and easier for our customers,” the spokesperson wrote.
An AWS Snowmobile truck appears in a Seattle parking lot in 2019.
Andrew Evers | CNBC
Snowmobile was priced at $0.005 gigabytes per month, not including other costs, according to a page formerly on the AWS website. For a company with 100 petabytes of data — the capacity of a Snowmobile — a transfer job would cost about $500,000 per month.
Amazon’s decision to axe Snowmobile comes as Jassy implements cost cuts across the company to contend with lackluster sales growth. Amazon has slashed more than 27,000 jobs since late 2022 and has discontinued projects in the devices and retail units. The cuts have continued this year, with Amazon laying off hundreds of jobs in AWS earlier this month.
While it’s fairly routine for AWS and rivals Microsoft Azure and Google Cloud Platform to get rid of products and services, the elimination of Snowmobile stands out due to the splashy way it was introduced at the company’s showcase Reinvent conference in Las Vegas in late 2016.
Jassy, who at the time led AWS, was delivering his keynote before tens of thousands of people in the crowd, when the 18-wheeler joined him on stage.
“We’re going to need a bigger box,” Jassy said, as audience members rushed to raise their smartphones to capture photos of the spectacle.
Jassy told the crowd why the truck was groundbreaking. Over a 10 gigabit-per-second connection, it would take 26 years to move an exabyte, or 1 million terabytes, of data to the cloud, he said. An AWS customer could do the job with 10 Snowmobiles in under six months, he said. Each Snowmobile had a capacity of 100 petabytes on hard disk drives.
In a blog post coinciding with the launch on Nov. 30, 2016, Amazon cloud evangelist Jeff Barr described Snowmobile as “a ruggedized, tamper-resistant shipping container 45 feet long, 9.6 feet high, and 8 feet wide” that “can be parked in a covered or uncovered area adjacent to your existing data center.”
Barr helped to convey the supposed simplicity of the process with photos of a Snowmobile built out of Lego getting connected to a corporate data center.
“We intend to make sure that Snowmobile is both faster and less expensive than using a network-based data transfer model,” Barr wrote.
But the product didn’t take off.
A spokesperson for satellite operator Maxar said the company used Snowmobile once in 2017 to move more than 100 petabytes to AWS from its own servers.
“Since then, we have been uploading our imagery and associated data directly to the cloud,” the spokesperson said.
AWS still leads the giant cloud infrastructure market and generated $90.8 billion in revenue last year, accounting for 16% of Amazon’s total sales. The company’s spokesperson said AWS’ Snowball Edge devices, which clients can return to Amazon by mail after filling them up with data, are smaller than the Snowmobile vehicles, cost less and have a shorter turnaround time.
There’s also the AWS DataSync service for moving data, announced in 2018. Clients generally find that sending data to AWS online is more economical than using Snowmobile, the company said.
“We couldn’t be more proud of the value that Snowmobile has brought to customers, and we’re pleased to see them choosing newer, more efficient technologies,” the spokesperson wrote.
If the U.S. continues to impose AI semiconductor restrictions on China, then chipmaker Huawei will take advantage of its position in the world’s second-largest economy, Nvidia CEO Jensen Huang told CNBC Thursday.
“Our technology is a generation ahead of theirs,” Huang told CNBC at the sidelines of the Viva Technology conference in Paris.
However, he warned that: “If the United States doesn’t want to partake, participate in China, Huawei has got China covered, and Huawei has got everybody else covered.”
In the face of U.S. export curbs that restrict Chinese firms from buying advanced semiconductors used in the development of AI, Beijing has focused on nurturing domestic firms such as Huawei in a bid to build its own AI chip ecosystem.
Huawei CEO Ren Zhengfei this week told the People’s Daily Newspaper of the governing Communist party that Huawei’s single chip is still behind the U.S. by a generation.
“The United States has exaggerated Huawei’s achievements. Huawei is not that great. We have to work hard to reach their evaluation,” Ren said in comments reported by Reuters.
This is a developing news story and will be updated shortly.
Jensen Huang, co-founder and chief executive officer of Nvidia Corp., left, and Emmanuel Macron, France’s president at the 2025 VivaTech conference in Paris, France, on Wednesday, June 11, 2025.
Nathan Laine | Bloomberg | Getty Images
French President Emmanuel Macron on Wednesday made a pitch for his country to manufacture the most advanced chips in the world, in a bid to position itself as a critical tech hub in Europe.
The comments come as European tech companies and countries are reassessing their reliance on foreign technology firms for critical technology and infrastructure.
Chipmaking in particular arose as a topic after Nvidia CEO Jensen Huang, who was doing a panel talk alongside Macron and Mistral AI CEO Arthur Mensch, said on Wednesday that the company’s first graphics processing unit (GPU) was manufactured in France by SGS Thomson Microelectronics, now known as STMicroelectronics.
Yet STMicroelectronics is currently not at the leading edge of semiconductor manufacturing. Most of the chips it makes are for industries like the automotive one, which don’t required the most cutting-edge semiconductors.
Macron nevertheless laid his ambition out for France to be able to manufacture semiconductors in the range of 2 nanometers to 10 nanometers.
“If we want to consolidate our industry, we have now to get more and more of the chips at the right scale,” Macron said on Wednesday.
The smaller the nanometer number, the more transistors that can be fit into a chip, leading to a more powerful semiconductor. Apple’s latest iPhone chips, for instance, are based on 3 nanometer technology.
Very few companies are able to manufacture chips at this level and on a large scale, with Samsung and Nvidia provider Taiwan Semiconductor Manufacturing Co. (TSMC) leading the pack.
If France wants to produce these cutting-edge chips, it will likely need TSMC or Samsung to build a factory locally — something that has been happening in the U.S. TSMC has now committed billions of dollars to build more factories Stateside.
Macron touted a deal between Thales, Radiall and Taiwan’s Foxconn, which are exploring setting up a semiconductor assembly and test facility in France.
One key partnership announced by Huang is between Nvidia and French AI model firm Mistral to build a so-called “AI cloud.”
France has looked to build out its AI infrastructure and Macron in February said that the country’s AI sector would receive 109 billion euros ($125.6 billion) in private investments in the coming years. Macron touted the Nvidia and Mistral deal as an extension of France’s AI buildout.
“We are deepening them [investments] and we are accelerating. And what Mistral AI and Nvidia announced this morning is a game-changer as well,” Macron told CNBC on Wednesday.
With the U.S. restricting China from buying advanced semiconductors used in artificial intelligence development, Beijing is placing hopes on domestic alternatives such as Huawei.
The task has been made more challenging by the fact that U.S. curbs not only inhibit China’s access to the world’s most advanced chips, but also restrict availing technology vital for creating an AI chip ecosystem.
Those constraints span the entire semiconductor value chain, ranging from design and manufacturing equipment used to produce AI chips to supporting elements such as memory chips.
Beijing has mobilized tens of billions of dollars to try to fill those gaps, but while it has been able to “brute force” its way into some breakthroughs, it still has a long way to go, according to experts.
“U.S. export controls on advanced Nvidia AI chips have incentivized China’s industry to develop alternatives, while also making it more difficult for domestic firms to do so,” said Paul Triolo, partner and senior vice president for China at advisory firm DGA-Albright Stonebridge Group.
Here’s how China stacks up against the rest of the world in four key segments needed to build AI chips.
AI chip design
Nvidia is regarded as the world’s leading AI chip company, but it’s important to understand that it doesn’t actually manufacture the physical chips that are used for AI training and computing.
Rather, the company designs AI chips, or more precisely, graphics processing units. Orders of the company’s patented GPU designs are then sent to chip foundries — manufacturers that specialize in the mass production of other companies’ semiconductor products.
While American competitors such as AMD and Broadcom offer varying alternatives, GPU designs from Nvidia are widely recognized as the industry standard. The demand for Nvidia chips is so strong that Chinese customers have continued to buy any of the company’s chips they can get their hands on.
But Nvidia is grappling with Washington’s tightening restrictions. The company revealed in April that additional curbs had prevented it from selling its H20 processor to Chinese clients.
Nvidia’s H20 was a less sophisticated version of its H100 processor, designed specifically to skirt previous export controls. Nevertheless, experts say, it was still more advanced than anything available domestically. But China hopes to change that.
In response to restrictions, more Chinese semiconductor players have been entering the AI processor arena. They’ve included a wide array of upstarts, such as Enflame Technology and Biren Technology, seeking to soak up billions of dollars in GPU demand left by Nvidia.
But no Chinese firm appears closer to providing a true alternative to Nvidia than Huawei’s chip design arm, HiSilicon.
Huawei’s most advanced GPU in mass production is its Ascend 910B. The next-generation Ascend 910C was reportedly expected to begin mass shipments as early as May, though no updates have emerged.
Dylan Patel, founder, CEO and chief analyst at SemiAnalysis, told CNBC that while the Ascend chips remain behind Nvidia, they show that Huawei has been making significant progress.
“Compared to Nvidia’s export-restricted chips, the performance gap between Huawei and the H20 is less than a full generation. Huawei is not far behind the products Nvidia is permitted to sell into China,” Patel said.
He added that the 910B was two years behind Nvidia as of last year, while the Ascend 910C is only a year behind.
But while that suggests China’s GPU design capabilities have made great strides, design is just one aspect that stands in the way of creating a competitive AI chip ecosystem.
AI chip fabrication
To manufacture its GPUs, Nvidia relies on TSMC, the world’s largest contract chip foundry, which produces most of the world’s advanced chips.
TSMC complies with U.S. chip controls and is also barred from taking any chip orders from companies on the U.S. trade blacklist. Huawei was placed on the list in 2019.
That has led to Chinese chip designers like Huawei to enlist local chip foundries, the largest of which is SMIC.
SMIC is far behind TSMC — it’s officially known to be able to produce 7-nanometer chips, requiring less advance tech than TSMC’s 3-nanometer production. Smaller nanometer sizes lead to greater chip processing power and efficiency.
There are signs that SMIC has made progress. The company is suspected to have been behind a 5-nanometer 5G chip for Huawei’s Mate 60 Pro, which had rocked confidence in U.S. chip controls in 2023. The company, however, has a long way to go before it can mass-produce advanced GPUs in a cost-efficient manner.
According to independent chip and technology analyst Ray Wang, SMIC’s known operation capacity is dwarfed by TSMC’s.
“Huawei is a very good chip design company, but they are still without good domestic chipmakers,” Wang said, noting that Huawei is reportedly working on its own fabrication capabilities.
But the lack of key manufacturing equipment stands in the way of both companies.
Advanced Chip equipment
SMIC’s ability to fulfill Huawei’s GPU requirements is limited by the familiar problem of export controls, but in this case, from the Netherlands.
While Netherlands may not have any prominent semiconductor designers or manufacturers, it’s home to ASML, the world’s leading supplier of advanced chipmaking equipment — machines that use light or electron beams to transfer complex patterns onto silicon wafers, forming the basis of microchips.
In accordance with U.S. export controls, the country has agreed to block the sale of ASML’s most advanced ultraviolet (EUV) lithography machines. The tools are critical to making advanced GPUs at scale and cost-effectively.
EUV is the most significant barrier for Chinese advanced chip production, according to Jeff Koch, an analyst at SemiAnalysis. “They have most of the other tooling available, but lithography is limiting their ability to scale towards 3nm and below process nodes,” he told CNBC.
SMIC has found methods to work around lithography restrictions using ASML’s less advanced deep ultraviolet lithography systems, which have seen comparatively fewer restrictions.
Through this “brute forcing,” producing chips at 7 nm is doable, but the yields are not good, and the strategy is likely reaching its limit, Koch said, adding that “at current yields it appears SMIC cannot produce enough domestic accelerators to meet demand.”
SiCarrier Technologies, a Chinese company working on lithography technology, has reportedly been linked to Huawei.
But imitating existing lithography tools could take years, if not decades, to achieve, Koch said. Instead, China is likely to pursue other technologies and different lithography techniques to push innovation rather than imitation, he added.
AI memory components
While GPUs are often identified as the most critical components in AI computing, they’re far from the only ones. In order to operate AI training and computing, GPUs must work alongside memory chips, which are able to store data within a broader “chipset.”
In AI applications, a specific type of memory known as HBM has become the industry standard. South Korea’s SK Hynix has taken the industry lead in HBM. Other companies in the field include Samsung and U.S.-based Micron.
“High bandwidth memory at this stage of AI progression has become essential for training and running AI models,” said analyst Wang.
As with the Netherlands, South Korea is cooperating with U.S.-led chip restrictions and began complying with fresh curbs on the sale of certain HBM memory chips to China in December.
In response, Chinese memory chip maker ChangXin Memory Technologies, or CXMT, in partnership with chip-packaging and testing company Tongfu Microelectronics, is in the early stages of producing HBM, according to a report by Reuters.
According to Wang, CXMT is expected to be three to four years behind global leaders in HBM development, though it faces major roadblocks, including export controls on chipmaking equipment.
SemiAnalysis estimated in April that CXMT remained a year away from ramping any reasonable volume.
Chinese foundry Wuhan Xinxin Semiconductor Manufacturing is reportedly building a factory to produce HBM wafers. A report from SCMP said that Huawei Technologies had partnered with the firm in producing HBM chips, although the companies did not confirm the partnership.
Huawei has leaned on HBM stockpiles from suppliers like Samsung for use in their Ascend 910C AI processor, SemiAnalysis said in an April report, noting that while the chip was designed domestically, it still relies on foreign products obtained prior to or despite restrictions.
“Whether it be HBM from Samsung, wafers from TSMC, or equipment from America, Netherlands, and Japan, there is a big reliance on foreign industry,” SemiAnalysis said.