US Market News
6日前
Tradr Launches Two Leveraged ETFs on Cerebras SystemsMay 28, 2026 4:30 PM
PR Newswire (US) Includes first-to-market inverse ETF that allows traders to take short view on this year's biggest IPO to dateNEW YORK, May 28, 2026 /PRNewswire/ -- Tradr ETFs, a provider of ETFs designed for sophisticated investors and professional traders, is launching two new leveraged ETFs. The Cboe-listed funds seek to deliver two times long (200%) and two times short (-200%) the daily performance on Cerebras Systems Inc. (Nasdaq: CBRS).The following ETFs are expected to open for trading today:Tradr 2X Long CBRS Daily ETF (Cboe: CBRX)Tradr 2X Short CBRS Daily ETF (Cboe: CBRZ)"Cerebras going public has added to the already high level of super-cycle excitement around semis as the space continues to be integral in the buildout of the AI economy," said Matt Markiewicz, Head of Product and Capital Markets at Tradr ETFs. "Its debut as the largest IPO thus far in 2026 has come with a lot of hype and expectation as well as speculation, characteristics that create volatility, and opportunity that attracts both bulls and bears."Tradr's lineup of 65 leveraged ETFs represents over $7 billion in assets under management. Tradr's strategies can be accessed through most brokerage platforms and allow investors to avoid the hassle of using margin and the complexity of options trading. The firm continues its mission of providing sophisticated investors with innovative trading tools that enhance their ability to express market views with precision and efficiency.For detailed information on Tradr ETFs and the significant risks involved with leveraged ETFs, please visit www.tradretfs.com.About Tradr ETFs
Tradr ETFs are designed for sophisticated investors and professional traders who are looking to express high conviction investment views. The strategies include leveraged and inverse ETFs that seek short or long exposure to actively traded stocks and ETFs.IMPORTANT RISK INFORMATION
Tradr ETFs are for sophisticated investors and professional traders with high conviction views and are very different from most other ETFs. The Funds are intended to be used as short-term trading vehicles and pursue leveraged investment objectives, which means they are riskier than alternatives that do not use leverage because the Funds magnify the performance of their underlying security. The volatility of the underlying security may affect a Fund's return as much as, or more than, the return of the underlying security.Investors in the fund should: (a) understand the risks associated with the use of leverage; (b) understand the consequences of seeking inverse and leveraged investment results; (c) for short ETFs, understand the risk of shorting; (d) intend to actively monitor and manage their investment. Fund performance will likely be significantly different than the benchmark over periods longer than the specified reset period and the performance may trend in the opposite direction than its benchmark over periods other than that period.Leverage increases the risk of a total loss of an investor's investment, may increase the volatility of the Funds, and may magnify any differences between the performance of the Funds and their reference security. The Funds seek leveraged investment results for a specific period (daily, monthly or quarterly). The exact exposure of an investment in the Fund intra-period will depend upon the movement of the reference security from the end of the prior period until the time of investment by the investor.The Fund will not attempt to position its portfolio to ensure it does not gain or lose more than a maximum percentage of its net asset value on a given trading day. As a consequence, investors in a Fund that seeks two times daily performance would lose all of their money if the Fund's underlying security moves more than 50% in a direction adverse to the Fund on a given trading day.ETFs involve risk including possible loss of the full principal value. There is no assurance that the Fund will achieve its investment objective. Principal risks and other important risks may be found in the prospectus. Past performance does not guarantee future results.ETF shares are bought and sold at market price (not NAV) and are not individually redeemed from the ETF. There can be no guarantee that an active trading market for ETF shares will develop or be maintained, or that their listing will continue or remain unchanged. Buying or selling ETF shares on an exchange may require the payment of brokerage commissions and frequent trading may incur brokerage costs that detract significantly from investment returns.Investors should carefully consider the investment objectives, risks, charges and expenses of the Funds. This and other important information about the Fund is contained in the Prospectus, which can be obtained by visiting www.tradretfs.com. The Prospectus should be read carefully before investing.Distributed by ALPS Distributors, Inc, which is not affiliated with AXS Investments or its Tradr ETFs. AXI000948 View original content to download multimedia:https://www.prnewswire.com/news-releases/tradr-launches-two-leveraged-etfs-on-cerebras-systems-302784986.htmlSOURCE Tradr ETFs Original: Tradr Launches Two Leveraged ETFs on Cerebras Systems
Oleblue
2週前
With Its IPO Done, Cerebras Can Get Back To Pushing The AI Envelope
Timothy Prickett Morgan
Co-Editor, Co-Founder, The Next Platform
Published Fri 15 May 2026 // 15:40 UTC
There will probably never be a better time for any AI-related company to go public than between right now and next summer. The GenAI frenzy is at a fever pitch, and the big four hyperscalers and cloud builders alone – Amazon Web Services, Google Cloud, Meta Platforms, and Microsoft Azure – have collectively projected for capital expenses to be somewhere between $695 billion and $725 billion in 2026. There is probably at least that much expected to be spent between the big AI model builders (who are starting to build their own datacenters), hyperscalers in China, plus sovereign AI centers, HPC centers, academic centers, and governments who are also wanting to get in on the GenAI action.
GenAI is a tactical and strategic weapon, both economically and militarily, and it is also a cultural force that has the potential to do great things as well as great harm to the established orders in these spheres.
Set against this backdrop, the hyperscalers and cloud builders are designing their own CPUs and XPUs or partnering with companies other than Nvidia and AMD to try to get better bang for the buck for AI inference workloads – or just to get any kind of matrix math compute at all.
It would have been hard for Cerebras Systems to pick a better day to go public and set the tone for the initial public offerings of Anthropic, OpenAI, and SpaceX, the latter of which has absorbed the xAI model building business that probably won’t be building Grok models in the future but is selling capacity on the Colossus-1 supercomputer in Memphis to rival Anthropic.
It is a classic “the enemy of my enemy is my friend” scenario, with no love lost between OpenAI and Musk, one of its founding investors who correctly observes that OpenAI was founded as a non-profit but then changed its mind. In the long run, SpaceX will probably build foundation models, or maybe Tesla will. Elon Musk is probably not done moving his pieces around the board, and it would not be surprising to see SpaceX and Tesla merged into one giant conglomerate doing self-driving cars, autonomous robots, and space launches, all of which need physical AI models more than they need GenAI models. If the Musk conglomerate needs a GenAI model, it can just use Anthropic’s Claude and be done with it, trading compute capacity for model access much as Microsoft did for many years with OpenAI.
The appetite for shares in Cerebras Systems, whose bankers sure did take their time getting co-founder and chief executive officer Andrew Feldman to ring the bell at the NASDAQ market, was huge, with an oversubscription of 25X for the 215.23 million shares that floated at $185 a pop, raising $5.55 billion for Cerebras. At the end of the day, the Cerebras shares were worth $311 per share, giving the public float of shares a market capitalization of $39.8 billion. If all of the shares and warrants in the company were taken into account, the market capitalization is about $95 billion.
That’s not too shabby for a company that had a $23 billion valuation after a $1 billion Series H fund raising round back in February.
With the IPO, Feldman’s 4.5 percent stake in Cerebras is worth $3.2 billion, while chief technology officer Sean Li has a 2.4 percent stake worth $1.7 billion.
We are not going to recap all of the financials for Cerebras, which we drilled down into back in April when the company refiled an S-1 in preparation for going public, something it had planned to do last year but put the kibosh on it because it was able to raise money through addition funding rounds. All told, including $1 billion from the Series H round, $1.3 billion in cash and marketable securities, and $1 billion in working capital from its $20 billion, 750 megawatt deal to install CS waferscale systems at OpenAI between now 2028 plus another 3 gigawatts of gear in 2029 and 2030. With the $5.55 billion infusion from the IPO, it has $8.9 billion in cash and equivalents. That is a good bit of money with which to build those systems for OpenAI as well as Mohamed bin Zayed University of Artificial Intelligence and G42, the two other big customers from the Middle East. The deal with Amazon Web Services has yet to be fully fleshed out, but we think it will happen and there is an outside chance that CS systems become the low latency inference boxes at AWS to complement its homegrown Trainium systems. It would not be surprising to see Anthropic ink a deal with Cerebras, too – and soon before Anthropic goes public so it can show it has the iron it needs to do low latency AI inference.
Here is what is very important about that big pile of cash that Cerebras now has. It is the successful innovator in waferscale chippery, and made something that several companies had tried to do and failed at. But the silicon wafers are not getting bigger at the same time that transistors are not getting dense enough fast enough, and whatever density and performance that Taiwan Semiconductor Manufacturing Co, Samsung, and Intel can bring to bear in their foundries, we are trapped on a 300 millimeter (12-inch) wafer and 450 millimeter (18-inch) wafers, an effort that failed a decade ago, is not going to happen. And even if that did happen, that would only get Cerebras another 50 percent more space to lay down compute and SRAM.
We think that with the WSE-4 waferscale chip, due perhaps later this year, Cerebras is going to have to go 3D and innovate on the Z axis much as it has done on the X and Y axes. When the low latency AI inference wars started in earnest a little more than a year ago, Cerebras and Groq alike had to gang up multiple machines together not for the compute, but because that was the only way to get enough SRAM in a system to get the model weights in memory close to the compute. At first, it was three CS-3 machines, then it was four, and then Cerebras stopped talking about the number when it gave out test results. So did Groq.
What is clear is that the compute to SRAM ratio on the WSE-3 waferscale processors is wrong for low latency inference. There are two ways to fix this. Shrink the process, cut back on the compute, and jack up the SRAM. It would be very difficult, however, to get 3X to 4X more SRAM onto a 2D square cut out of a 12-inch wafer. You would then have to interconnect these wafers to scale out the compute because there would be a lot less of it on each waferscale chip.
The other option, which we have seen both AMD and Intel do with their CPUs and GPUs, is to go vertical with the on-chip memory and stack it up. Stacked SRAM on top of the base WSE-4 wafer could easily solve this problem and boost the effective performance per WS engine such that an AI model may go back to fitting on a single device again for reasonably sized and still useful models. We think there is a high likelihood that the future WSE-4 will do at least this.
We have higher hopes for innovation, of course. We would like for the WS-4 to have optical links coming out of the wafer to shared DRAM memory trays to significantly expand the MemoryX capacity of the CS-4 system, and make the memory have its own network (as is done with GPUs these days with so-called scale up memory fabrics). Optical links using co-packaged optics could also be used to implement SwarmX clustering, boosting bandwidth between WSE devices significantly.
https://www.nextplatform.com/compute/2026/05/15/with-its-ipo-done-cerebras-can-get-back-to-pushing-the-ai-envelope/5241317?mc_cid=165f5c3749&mc_eid=9d91dde03c
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iHub News
3週前
Cerebras Jumps 69% in Nasdaq Debut as AI IPO Market Roars Back (CBRS)May 15, 2026 6:01 AM
IH Market News May 14, 2026 marked a major moment for artificial intelligence listings, with Cerebras Systems (NASDAQ:CBRS) surging 68% on its first day of trading on the Nasdaq to close at $311.07, well above its IPO price of $185 per share. The flotation generated $5.55 billion, making it one of the biggest U.S. technology IPO fundraisings in recent years. AI Chip Specialist Draws Investor Attention Established in 2016 in Sunnyvale, California, Cerebras develops processors tailored for artificial intelligence computing, with a particular focus on inference workloads, where AI systems generate responses to user prompts. Its flagship Wafer Scale Engine 3 differs from traditional chip architectures by being built on a single silicon wafer rather than multiple interconnected chips, unlike Nvidia’s GPU-based approach. The company says this structure delivers advantages in both processing speed and operational efficiency for AI inference tasks. Revenue Growth and Profitability Fuel Momentum Cerebras reported revenue of $510 million in 2025, representing year-on-year growth of 76%. The company also returned to profitability, posting net income of $237.8 million after recording a loss of nearly $500 million the previous year.Investor appetite has also been boosted by a number of strategic agreements, including a multi-year contract with OpenAI reportedly valued at more than $20 billion, as well as a partnership with Amazon Web Services announced in March. Valuation Nears $100 Billion Following its explosive market debut, Cerebras is now approaching a market valuation of nearly $100 billion. That compares with a valuation of $23.1 billion during a private fundraising round completed in February, underlining continued strong investor demand for companies tied to AI infrastructure and next-generation computing.Cerebras Systems stock price Original: Cerebras Jumps 69% in Nasdaq Debut as AI IPO Market Roars Back (CBRS)
Oleblue
3月前
Cerebras founder and chief architect Michael James walks through the CS-3 system and its wafer-scale engine, a single 300 mm die integrating around a million AI-optimized compute cores on one piece of silicon. Built in 5 nm with roughly 4 trillion transistors, WSE-3 delivers on-chip memory, interconnect and compute in one monolithic device, targeting high-throughput AI inference and data-intensive HPC workloads in a compact rack-scale node. https://www.cerebras.ai/chip
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He explains the extreme power-delivery and packaging needed to run this chip at roughly 25 kW: front-side AC/DC modules, 3D power distribution and dense arrays of regulators positioned close to the wafer to manage around 30,000 amps of current. Because all compute and 44 GB of SRAM sit on a single wafer, the system minimizes off-chip traffic and uses control logic that smooths power ramps with dummy operations when workloads switch off, avoiding destructive current spikes while preserving energy efficiency.
On the architecture side, James describes the WSE-3 as a proprietary dataflow processor designed for strong scaling. Loop induction variables, data movement and network behavior are encoded directly into the instruction set, so a single matrix operation can be spread spatially across the full grid of cores with minimal software overhead. That allows Cerebras to map full transformer layers over the wafer and reach very high inference throughput, with customers reporting large speedups over Nvidia GPU clusters on latency-sensitive language-model serving.
The discussion then shifts to real workloads, including a global shallow-water-equation simulation of an asteroid impact off California, run at about 200 m resolution over the entire planet. By exploiting the dense on-wafer memory and mesh interconnect, a cluster of CS-3 nodes achieved exascale-class performance for this tsunami scenario at a fraction of the power draw of traditional exascale systems, while still supporting large language models such as Llama and DeepSeek on the same architecture.
Filmed at Supercomputing 2025 in St Louis, the interview also touches on manufacturing yield and roadmap. Cerebras overprovisions identical cores across the wafer and then uses automated defect mapping plus constraint solving to reroute communication around faulty regions, guaranteeing at least 900,000 working cores per device and turning the rest into pass-through fabric. James hints that future generations will continue this wafer-scale path, pushing AI inference and physics-based HPC further by co-designing architecture, packaging and dataflow software as a single system.
Cerebras CS-3 wafer-scale million-core AI chip, 25kW WSE-3, 125 PFLOPS inference engine, tsunami HPC
Oleblue
2年前
Cerebras Systems Announces Filing of Registration Statement for Proposed Initial Public Offering
September 30, 2024
SUNNYVALE, Calif.–(BUSINESS WIRE)–Cerebras Systems (“Cerebras”) today announced that it has filed a registration statement on Form S-1 with the U.S. Securities and Exchange Commission (“SEC”) relating to a proposed initial public offering of its Class A common stock. The number of shares of Class A common stock to be offered and the price range for the proposed offering have not yet been determined. The offering is subject to market conditions, and there can be no assurance as to whether or when the offering may be completed, or as to the actual size or other terms of the offering.
Cerebras intends to list its Class A common stock on the Nasdaq Global Market under the ticker symbol “CBRS.”
Citigroup and Barclays are acting as lead book-running managers for the proposed offering. UBS Investment Bank, Wells Fargo Securities, Mizuho and TD Cowen are also acting as book-running managers. Needham & Company, Craig-Hallum, Wedbush Securities, Rosenblatt and Academy Securities are acting as co-managers.
The proposed offering will be made only by means of a prospectus. Copies of the preliminary prospectus related to this offering, when available, may be obtained from: Citigroup Global Markets Inc., c/o Broadridge Financial Solutions, 1155 Long Island Avenue, Edgewood, NY 11717, or telephone: 800-831-9146 and Barclays Capital Inc., c/o Broadridge Financial Solutions, 1155 Long Island Avenue, Edgewood, NY 11717, by email at barclaysprospectus[ @LoveFishing-5847.
A registration statement relating to these securities has been filed with the SEC but has not yet become effective. These securities may not be sold, nor may offers to buy be accepted, prior to the time the registration statement becomes effective.
This press release shall not constitute an offer to sell or the solicitation of an offer to buy these securities, nor shall there be any sale of these securities in any state or jurisdiction in which such offer, solicitation or sale would be unlawful prior to registration or qualification under the securities laws of any such state or jurisdiction.
About Cerebras Systems
Cerebras Systems is a team of pioneering computer architects, computer scientists, deep learning researchers, and engineers of all types. We have come together to accelerate generative AI by building a new class of AI supercomputer. Our flagship product, the CS-3 system, is powered by the largest commercially available AI processor, our Wafer-Scale Engine-3. CS-3s are quickly and easily clustered together to make massive AI supercomputers, and make placing models on the supercomputers simple by avoiding the complexity of distributed computing. Leading enterprises, research institutions, and government agencies use Cerebras solutions for the development of pathbreaking proprietary models, and to train open-source models that have achieved over a million downloads. Cerebras solutions are available through the Cerebras Cloud and on premises.
Contacts
Media
ZM Communications
Pr@zmcommunications.com
Investors
Cerebras Investor Relations
Sean Dorsey
sean.dorsey@cerebras.net
Oleblue
2年前
New AI Chip Leaves Nvidia, AMD, and Intel in the Dust with 20x Faster Speeds and Over 4 Trillion Transistors
Story by Caleb Naysmith
player: Cerebras Systems. The California-based startup recently unveiled Cerebras Inference, a cutting-edge solution reportedly up to 20 times faster than Nvidia (NVDA) GPUs, sparking attention across the tech landscape.
Cerebras’ breakthrough innovation, the Wafer Scale Engine, now in its third generation, powers this new Cerebras Inference system. This enormous chip packs 44GB of SRAM and requires no external memory, eliminating a key bottleneck found in traditional GPU setups. By addressing memory bandwidth limitations, Cerebras Inference achieves impressive speeds—processing 1,800 tokens per second for Llama3.1 8B and 450 tokens for Llama3.1 70B—setting a new performance standard in the industry.
For investors and tech enthusiasts, comparing Cerebras with established chipmakers like Nvidia, Advanced Micro Devices (AMD), and Intel (INTC) is becoming increasingly relevant. While Nvidia has traditionally led the AI hardware space with its advanced GPU solutions, Cerebras’ disruptive technology presents a formidable alternative. Meanwhile, AMD and Intel, both long-standing players in the chip industry, may also face increased competition as Cerebras gains traction in high-performance AI applications.
Cerebras Chips vs. Nvidia: A Technical Comparison
When comparing Cerebras and Nvidia, several crucial factors stand out, including design, performance, application suitability, and potential market impact.
Architectural Design
·Cerebras: The Wafer Scale Engine from Cerebras is unique—built on a single, massive wafer with approximately 4 trillion transistors and 44GB of on-chip SRAM. This design eliminates reliance on external memory, bypassing the memory bandwidth constraints of conventional architectures. Cerebras aims to provide the largest, most powerful chip that can house and manage enormous AI models directly on the wafer, significantly reducing latency.
·Nvidia: Nvidia’s architecture, meanwhile, uses a multi-die approach where several GPU dies are connected via high-speed interlinks such as NVLink. This setup, showcased in products like the DGX B200 server, provides a modular and scalable solution, though it requires intricate coordination between multiple chips and memory systems. Nvidia’s GPUs, refined over years, are optimized for both AI training and inference tasks, maintaining a competitive edge in versatility.
Performance
·Cerebras: In AI inference tasks, Cerebras Inference shines by processing inputs reportedly 20 times faster than Nvidia’s comparable solutions. The on-chip memory and processing integration enable high-speed data access and processing without the delays associated with chip-to-chip data transfers.
·Nvidia: While Nvidia may not match Cerebras’ raw speed for inference tasks, its GPUs are versatile workhorses across multiple applications, from gaming to complex AI training. Nvidia’s strength lies in its robust ecosystem and mature software stack, making its GPUs well-suited for a wide range of AI tasks and beyond.
Application Suitability
·Cerebras: Cerebras chips are especially suitable for enterprises with large-scale AI models requiring ultra-fast processing, such as natural language processing and deep learning inference. This solution is ideal for organizations that prioritize minimizing latency and need real-time processing of large datasets.
·Nvidia: Nvidia’s GPUs are more adaptable, capable of handling a broad range of tasks, from video game graphics to advanced AI model training and simulations. This versatility makes Nvidia a reliable choice for diverse sectors, not solely those focused on AI.
Conclusion
Cerebras offers standout performance in specific, high-demand AI tasks, while Nvidia excels with its versatility and robust ecosystem. The choice between Cerebras and Nvidia ultimately depends on particular needs: Cerebras could be an optimal choice for organizations handling extremely large AI models where inference speed is paramount. On the other hand, Nvidia continues to be a strong competitor across various applications, backed by its flexible hardware and comprehensive software support.
https://www.msn.com/en-us/news/technology/new-ai-chip-leaves-nvidia-amd-and-intel-in-the-dust-with-20x-faster-speeds-and-over-4-trillion-transistors/ar-AA1t3OPe?ocid=hpmsn&cvid=6662c44bee7c4861b1a4d70952599fe5&ei=5
Looks like it is PRIVATE so good luck.
https://cerebras.ai/
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