Latest news with #hardware
Forbes
21 hours ago
- Business
- Forbes
Quantum, Moore's Law, And AI's Future
microchip integrated on motherboard In the game of AI acceleration, there are several key moving parts. One of them is hardware: what do the chips look like? And this is a very interesting question. Another is quantum computing: what role will it play? Another is scaling. Everyone from CEOs and investors to engineers is scrambling to figure out what the future looks like, but we got a few ideas from a recent panel at Imagination in Action that assembled some of the best minds on the matter. WSE and the Dinner Plate of Reasoning Not too long ago, I wrote about the Cerebras WSE chip, a mammoth piece of silicon about the size of a dinner plate, that is allowing the centralization of large language model efforts. This is an impressive piece of hardware by any standard, and has a role in coalescing the vanguard of what we are doing with AI hardware. In the aforementioned panel discussion, Julie Choi from Cerebras started by showing off the company's WSE superchip, noting that some call it the 'caviar of inference.' (I thought that was funny.) 'I think that as we evolve, we're just going to see even more innovative, novel approaches at the hardware architecture level,' she said. 'The optimization space is extremely large,' said Dinesh Maheshwari, discussing architecture and compute units. 'So I encourage everyone to look at it.' Panelist Caleb Sirak, also of MIT, talked about ownership of hardware. 'As the models themselves start to change, how can businesses themselves integrate them directly and get them for a fair price, but also convert that AI, and the energy involved, into a productive utility?' 'What is a computer, and what can a computer do?' asked Alexander Keesling, explaining his company's work on hardware. 'We took the fundamental unit of matter, a single atom, and turned it into the fundamental unit of information, which is a quantum bit … a quantum computer is the first time in human history where we can take advantage of the fundamental properties of nature to do something that is different and more powerful.' Jeremy Kepner of MIT's Lincoln Lab had some thoughts on the singularity of computing – not the race toward AGI, but a myopic centralization of an overarching 'operation.' 'Every single computer in the high end that we built for the last many decades has only done one operation,' he said. 'So there's a lot to unpack there, but it's for very deep mathematical and physics reasons: that's the only operation we've ever been able to figure out how to accelerate over many decades. And so what I often tell the users is, the computer picks the application. AI happens to be acceleratable by that operation.' He urged the audience to move forward in a particular way. 'Think about whatever you want to do, and if you can accelerate it with that kind of mathematical operation, you know the sky is the limit on what you can do,' he said. 'And someone in your field will figure it out, and they will move ahead dramatically.' Engineering Challenges and AI Opportunities The panel also mentioned some of the headwinds that innovators must contend with. On the other hand, Jeff Grover noted the near-term ability of systems to evolve. 'We're actually quite excited about this,' he said. The Software End Panelists discussed the relevance of software and the directions that coding is going in. 'Programming languages are built for people,' Sirak said. 'How do you actually change that to build languages and tools that AI can use?' Choi mentioned benchmarks like inference rates of 2900 tokens per second for Llama 4. 'Open source models are rich for developers,' she said. 'What that's doing is building a bridge between the bravest developers. I would say the early adopters tend to be very courageous, and they're willing to code on things that they've never seen before.' The Fast Car Several panelists talked about a particular metaphor to a Ferrari, with Choi referencing 'Ferrari-level' speeds for the Cerebras chip. Maheshwari talked about 'exotic' chips, and design from an architecture paradigm, comparing certain builds to 'picking up groceries in a Ferrari.' He also mentioned the imperative of keeping the technology 'street legal.' Moore's Law and Progress Kepner talked about being surprised by what computers can do, and the size of investment in the industry. Moore's law, he said, implied an upper limit for spending. He predicted another decade of efficiencies, and cited the Ozaki scheme, a matrix method for preserving precision in calculations. What About Quantum? 'I think that the first area where we're going to see quantum computing impact is going to be in research,' Keesling said. 'These problems, at their core, are (about) trying to answer what happens when atoms and electrons interact with one another and develop these emergent behaviors … how we think about chemistry, how we think about drug interactions, how we think about material properties, all comes from electrons and atoms moving.' There was a lot to unpack in this panel discussion, including details on how we're going to achieve progress in the next few years. The Ozaki Scheme Going back to this matrix idea, I was not familiar with this term, so I looked it up and asked ChatGPT to describe it in basic English. 'It's named after Makoto Ozaki, the person who came up with the idea,' the model told me. 'He found a smart way to do very accurate math (like multiplying big grids of numbers) using fast but less accurate tools (like low-precision numbers). His method splits the work into small, simple steps and then carefully puts the pieces back together to get the exact right answer.' Going further, ChatGPT, just to be nice, even gave me a medieval storyline to show how the Ozaki scheme works, and to contrast it to other alternatives. I'm just going to print that here, because it's interesting. The Tale of the Kingdom of Matrixland In the kingdom of Matrixland, the royal court has a big job: multiplying giant tables of numbers (called matrices). But the royal calculator is slow when it uses fancy, high-precision numbers. So the King holds a contest: 'Who can multiply big matrices both quickly and accurately?' Sir Ozaki's Clever Trick Sir Ozaki, a wise mathematician, enters the contest. He says: 'I'll break each matrix into small, easy pieces that the royal calculator can handle quickly. Then I'll multiply those simple parts and put them back together perfectly.' The crowd gasps! His method is fast and still gives the exact right answer. The King declares it the Ozaki Scheme. The Other Contestants But other knights have tricks too: Lady Refina (Iterative Refinement) She does the quick math first, then checks her work. If it's off, she fixes it — again and again — until it's just right. She's very accurate, but takes more time. Sir Compenso (Compensated Summation) He notices small errors that get dropped during math and catches them before they vanish. He's good at adding accurately, but can't handle full matrix multiplication like Ozaki. Lady Mixie (Mixed Precision) She charges in with super speed, using tiny fast numbers (like FP8 or FP16). Her answers aren't perfect, but they're 'good enough' for training the kingdom's magical beasts (AI models). Baron TensorFloat (TF32) He uses a special number format invented by the kingdom's engineers. Faster than full precision, but not as sharp as Ozaki. A favorite of the castle's GPU-powered wizard lab. The Ending Sir Ozaki's method is the most exact while still using fast tools. Others are faster or simpler, but not always perfect. The King declares: 'All of these knights are useful, depending on the task. But if you want both speed and the exact answer, follow Sir Ozaki's path!' Anyway, you have a range of ideas here about quantum computing, information precision, and acceleration in the years to come. Let me know what you think about what all of these experts have said about the future of AI.
Forbes
a day ago
- Business
- Forbes
AMD Keeps Building Momentum In AI, With Plenty Of Work Still To Do
At the AMD Advancing AI event, CEO Lisa Su touted the company's AI compute portfolio. At the AMD Advancing AI event in San Jose earlier this month, CEO Lisa Su and her staff showcased the company's progress across many different facets of AI. They had plenty to announce in both hardware and software, including significant performance gains for GPUs, ongoing advances in the ROCm development platform and the forthcoming introduction of rack-scale infrastructure. There were also many references to trust and strong relationships with customers and partners, which I liked, and a lot of emphasis on open hardware and an open development ecosystem, which I think is less of a clear winner for AMD, as I'll explain later. Overall, I think the event was important for showing how AMD is moving the ball down the field for customers and developers. Under Su, AMD's M.O. is to have clear, ambitious plans and execute against them. Her 'say/do' ratio is high. The company does what it says it will do. This is exactly what it must continue doing to whittle away at Nvidia's dominance in the datacenter AI GPU market. What I saw at the Advancing AI event raised my confidence from last year — although there are a few gaps that need to be addressed. (Note: AMD is an advisory client of my firm, Moor Insights & Strategy.) AMD's AI Market Opportunity And Full-Stack Strategy When she took the stage, Su established the context for AMD's announcements by describing the staggering growth that is the backdrop for today's AI chip market. Just take a look at the chart below. So far, AMD's bullish projections for the growth of the AI chip market have turned out to be ... More accurate. So this segment of the chip industry is looking at a TAM of half a trillion dollars by 2028, with the whole AI accelerator market increasing at a 60% CAGR. The AI inference sub-segment — where AMD competes on better footing with Nvidia — is enjoying an 80% CAGR. People thought that the market numbers AMD cited last year were too high, but not so. This is the world we're living in. For the record, I never doubted the TAM numbers last year. AMD is carving out a bigger place in this world for itself. As Su pointed out, its Instinct GPUs are used by seven of the 10 largest AI companies, and they drive AI for Microsoft Office, Facebook, Zoom, Netflix, Uber, Salesforce and SAP. Its EPYC server CPUs continue to put up record market share (40% last quarter), and it has built out a full stack — partly through smart acquisitions — to support its AI ambitions. I would point in particular to the ZT Systems acquisition and the introduction of the Pensando DPU and the Pollara NIC. GPUs are at the heart of datacenter AI, and AMD's new MI350 series was in the spotlight at this event. Although these chips were slated to ship in Q3, Su said that production shipments had in fact started earlier in June, with partners on track to launch platforms and public cloud instances in Q3. There were cheers from the crowd when they heard that the MI350 delivers a 4x performance improvement over the prior generation. AMD says that its high-end MI355X GPU outperforms the Nvidia B200 to the tune of 1.6x memory, 2.2x compute throughput and 40% more tokens per dollar. (Testing by my company Signal65 showed that the MI355X running DeepSeek-R1 produced up to 1.5x higher throughput than the B200.) To put it in a different perspective, a single MI355X can run a 520-billion-parameter model. And I wasn't surprised when Su and others onstage looked ahead to even better performance — maybe 10x better — projected for the MI400 series and beyond. That puts us into the dreamland of an individual GPU running a trillion-parameter model. By the way, AMD has not forgotten for one second that it is a CPU company. The EPYC Venice processor scheduled to hit the market in 2026 should be better at absolutely everything — 256 high-performance cores, 70% more compute performance than the current generation and so on. EPYC's rapid gains in datacenter market share over the past few years are no accident, and at this point all the company needs to do for CPUs is hold steady on its current up-and-to-the-right trajectory. I am hopeful that Signal65 will get a crack at testing the claims the company made at the event. This level of performance is needed in the era of agentic AI and a landscape of many competing and complementary AI models. Su predicts — and I agree — that there will be hundreds of thousands of specialized AI models in the coming years. This is specifically true for enterprises that will have smaller models focused on areas like CRM, ERP, SCM, HCM, legal, finance and so on. To support this, AMD talked at the event about its plan to sustain an annual cadence of Instinct accelerators, adding a new generation every year. Easy to say, hard to do — though, again, AMD has a high say/do ratio these days. AMD's 2026 Rack-Scale Platform And Current Software Advances On the hardware side, the biggest announcement was the forthcoming Helios rack-scale GPU product that AMD plans to deliver in 2026. This is a big deal, and I want to emphasize how difficult it is to bring together high-performing CPUs (EPYC Venice), GPUs (MI400) and networking chips (next-gen Pensando Vulcano NICs) in a liquid-cooled rack. It's also an excellent way to take on Nvidia, which makes a mint off of its own rack-scale offerings for AI. At the event, Su said she believes that Helios will be the new industry standard when it launches next year (and cited a string of specs and performance numbers to back that up). It's good to see AMD provide a roadmap this far out, but it also had to after Nvidia did at the GTC event earlier this year. On the software side, Vamsi Boppana, senior vice president of the Artificial Intelligence Group at AMD, started off by announcing the arrival of ROCm 7, the latest version of the company's open source software platform for GPUs. Again, big improvements come with each generation — in this case, a 3.5x gain in inference performance compared to ROCm 6. Boppana stressed the very high cadence of updates for AMD software, with new features being released every two weeks. He also talked about the benefits of distributed inference, which allows the two steps of inference to be tasked to separate GPU pools, further speeding up the process. Finally, he announced — to a chorus of cheers — the AMD Developer Cloud, which makes AMD GPUs accessible from anywhere so developers can use them to test-drive their ideas. Last year, Meta had kind things to say about ROCm, and I was impressed because Meta is the hardest 'grader' next to Microsoft. This year, I heard companies talking about both training and inference, and again I'm impressed. (More on that below.) It was also great getting some time with Anush Elangovan, vice president for AI software at AMD, for a video I shot with him. Elangovan is very hardcore, which is exactly what AMD needs. Real grinders. Nightly code drops. What's Working Well For AMD in AI So that's (most of) what was new at AMD Advancing AI. In the next three sections, I want to talk about the good, the needs-improvement and the yet-to-be-determined aspects of what I heard during the event. Let's start with the good things that jumped out at me. What Didn't Work For Me At Advancing AI While overall I thought Advancing AI was a win for AMD, there were two areas where I thought the company missed the mark — one by omission, one by commission. The Jury Is Out On Some Elements Of AMD's AI Strategy In some areas, I suspect that AMD is doing okay or will be doing okay soon — but I'm just not sure. I can't imagine that any of the following items has completely escaped AMD's attention, but I would recommend that the company address them candidly so that customers know what to expect and can maintain high confidence in what AMD is delivering. What Comes Next In AMD's AI Development It is very difficult to engineer cutting-edge semiconductors — let alone rack-scale systems and all the attendant software — on the steady cadence that AMD is maintaining. So kudos to Su and everyone else at the company who's making that happen. But my confidence (and Wall Street's) would rise if AMD provided more granularity about what it's doing, starting with datacenter GPU forecasts. Clearly, AMD doesn't need to compete with Nvidia on every single thing to be successful. But it would be well served to fill in some of the gaps in its story to better speak to the comprehensive ecosystem it's creating. Having spent plenty of time working inside companies on both the OEM and semiconductor sides, I do understand the difficulties AMD faces in providing that kind of clarity. The process of landing design wins can be lumpy, and a few of the non-AMD speakers at Advancing AI mentioned that the company is engaged in the 'bake-offs' that are inevitable in that process. Meanwhile, we're left to wonder what might be holding things back, other than AMD's institutional conservatism — the healthy reticence of engineers not to make any claims until they're sure of the win. That said, with Nvidia's B200s sold out for the next year, you'd think that AMD should be able to sell every wafer it makes, right? So are AMD's yields not good enough yet? Or are hyperscalers having their own problems scaling and deploying? Is there some other gating item? I'd love to know. Please don't take any of my questions the wrong way, because AMD is doing some amazing things, and I walked away from the Advancing AI event impressed with the company's progress. At the show, Su was forthright about describing the pace of this AI revolution we're living in — 'unlike anything we've seen in modern computing, anything we've seen in our careers, and frankly, anything we've seen in our lifetime.' I'll keep looking for answers to my nagging questions, and I'm eager to see how the competition between AMD and Nvidia plays out over the next two years and beyond. Meanwhile, AMD moved down the field at its event, and I look forward to seeing where it is headed.
Digital Trends
2 days ago
- Digital Trends
Building a gaming PC from scratch? Save over $400 with this components bundle
Is it time to upgrade your gaming rig? Whether you're looking to replace some key components or building the entire thing from scratch, you won't want to miss this Gigabyte bundle. Right now you can get a CPU, GPU, RAM and motherboard for $2,400, down $438 from their initial price. This is part of the NextGen X3D System Bundle at Canada Computers, and it runs until Monday, June 30. These are some great components that we've praised in the past, including a CPU we heralded as the 'king of gaming.' Let's check out each component. Why you should buy the NextGen X3D System Bundle This bundle gets you four major components you need for building a gaming PC. It's not everything, but it will get you very close. Let's break down each piece. CPU: AMD Rizen 7 7800X3D. This bundle starts out with a bang. The 7800X3D CPU was crowned the 'king of gaming' when it came out, and still holds a spot in our list of the best CPUs for gaming as the 'best last-gen processor for gaming.' It's a very fast processor that will hold up even though it's technically a generation behind. GPU: Gigabyte GeForce RTX 5080. The 5080 didn't get the most glowing review when it came out, but it has its place among the newest generation of Nvidia graphics cards. The 50-Series GPUs now use AI Management Processor (AMP), which increases speeds by scheduling tasks across the 10,752 CUDA cores. Motherboard: Gigabyte B650 Eagle AX. This motherboard has lots of input options, Wi-Fi 6E capabilities, and Bluetooth. It has three M.2 slots for SSDs, so you could potentially add a third 16GB SSD for even more RAM. SSD: Corsair Vengeance RGB 32GB. This is double the standard 16GB of RAM suggested by our guide on how much RAM you need. That's a great start for a powerful gaming PC. If you're starting from scratch, you'll need a few more things — a case and some fans, for instance. Check out our guide on how to build a gaming PC for more details. If these components fit your needs, grab this deal before it ends on Monday, June 30. You can get this bundle for $2,400, saving you $438 over the original price.
Yahoo
5 days ago
- Business
- Yahoo
OpenAI just took down its page about io's ChatGPT hardware, but it's not canceled
If you purchase an independently reviewed product or service through a link on our website, BGR may receive an affiliate commission. I wouldn't blame you if you spent the weekend watching the news and scrolling through social media to see whether World War 3 had started. Along the way, you might have noticed people tweeting about a puzzling move from OpenAI. The company removed any mention of Jony Ive's io startup, which OpenAI recently acquired for $6.5 billion to manufacture ChatGPT hardware. The announcement video featuring Jony Ive and Sam Altman disappeared. That was the clip where both executives confirmed OpenAI is developing its own AI hardware and shared the first teaser of the device. Today's Top Deals Best deals: Tech, laptops, TVs, and more sales Best Ring Video Doorbell deals Memorial Day security camera deals: Reolink's unbeatable sale has prices from $29.98 Some people might have wondered if OpenAI suddenly scrapped plans to make ChatGPT hardware despite the massive investment. But the answer is much simpler. The ChatGPT io hardware is still happening, though OpenAI removed the information following a court order prompted by a trademark complaint from a company called iyO. OpenAI acknowledged the page's removal on X and told The Verge that the io deal is still moving forward. It had to take down the content due to the trademark issue. Here's OpenAI's statement: This page is temporarily down due to a court order following a trademark complaint from iyO about our use of the name 'io.' We don't agree with the complaint and are reviewing our options. I knew there had to be an explanation as soon as I saw people talking about OpenAI removing the Jony Ive video and press release. I've said it many times—OpenAI has no choice but to develop its own ChatGPT hardware. Without its own devices, OpenAI can't compete with Google, Apple, Meta, and other tech companies that are building AI-first platforms. I'm talking about products where advanced AI serves as a personal assistant, going well beyond anything we've seen before. Reports following the io acquisition made it clear that OpenAI isn't building a smartphone like the iPhone. Instead, the ChatGPT io hardware is designed to be worn around the neck or placed on a desk. The idea is that it'll be the third most important device in a person's life, after their iPhone and Mac. OpenAI is developing new AI technologies for the still-unnamed ChatGPT io device, including enhanced abilities for the chatbot. ChatGPT will need to be aware of the user's context, recognize who is speaking, and know when to respond. Altman said in the now-deleted video that he had tried a prototype and praised the device. You don't pull the plug on a project like this overnight. A lot of work has already gone into the ChatGPT io product. OpenAI has likely designed and tested new experiences tailored for first-party hardware. Plenty of capital has been invested. That's not something you just abandon. OpenAI hinted at an official product launch in 2026. We'll see if they deliver. I'm sure they'll resolve the trademark issue in one way or another. As for iyO, it's a company making computers you talk to: The iyo one is a revolutionary new kind of computer without a screen. It can run apps just like your smartphone. The key difference is you talk to it through a natural language interface. Yeah, it sounds a lot like what OpenAI is working on with ChatGPT. The company, which began as a Google X project and has backing from Alphabet, lists two products on its site: the iyO One ($99) and the Vad Pro ($1,999). Don't Miss: Today's deals: Nintendo Switch games, $5 smart plugs, $150 Vizio soundbar, $100 Beats Pill speaker, more More Top Deals Amazon gift card deals, offers & coupons 2025: Get $2,000+ free See the
Globe and Mail
5 days ago
- Globe and Mail
Esgaming Launches Z300B PC Gaming Case: A New Standard in Cooling, Compatibility, and Aesthetic Excellence
Esgaming, a leading global brand in high-performance gaming hardware, announces the launch of the Z300B PC Gaming Case, engineered to meet the evolving needs of gamers, DIY builders, and workstation users who demand both efficiency and elegance in their setups. Built with a focus on cooling performance, dust-proof design, and user convenience, the Z300B continues Esgaming's tradition of delivering purpose-built solutions that balance cutting-edge functionality with visual appeal. Engineered for Performance and Compatibility The Z300B is constructed from durable SPCC 1.0mm all-black steel and features a premium tempered glass side panel, creating a sleek yet sturdy enclosure suitable for high-performance builds. Designed for M-ATX and ITX motherboards, the case supports graphics cards up to 360mm and CPU coolers up to 162mm, making it a versatile choice for modern gaming and content creation systems. Exceptional Cooling and Dust Control The Z300B supports up to five 120mm fan mounts, including 2×120mm top, 1×120mm rear, and 2×120mm bottom, and offers compatibility with a 240mm radiator on the top panel. To protect internal components and extend system longevity, the case is equipped with front iron mesh and top and bottom metal panels, all paired with dust filters. This combination ensures efficient airflow and minimizes dust accumulation without compromising aesthetics. Smart Connectivity and Modular Design The front I/O panel includes a 1×USB 3.0 port and HD Audio, with an optional Type-C interface available for a nominal additional cost of $1.80, allowing users to customize their connectivity. A bottom-mounted ATX power supply (up to 155mm) and 13mm of cable management space ensure easy routing and clean internal layouts. The modular structure is designed for quick installation and effortless maintenance, with removable dust filters and clearly defined component mounting zones simplifying the building process for users of all skill levels. Sleek, Functional, and Built for Enthusiasts With its minimalistic black finish and transparent side panel, the Z300B appeals to a wide range of users, including gamers, PC enthusiasts, and budget-conscious builders who want top-tier performance without sacrificing style. Customers have praised the Z300B for its excellent cooling capacity, ease of installation, and overall design sophistication. Precision Manufacturing Backed by Quality Assurance Each Z300B unit undergoes a meticulous production process that includes steel and glass material cutting, stamping, surface coating, and multi-stage assembly of filters, fan brackets, and I/O components. All cases are subjected to comprehensive quality testing to ensure consistent structural integrity and performance. About Esgaming Esgaming is a professional gaming accessories brand known for its commitment to user-centered innovation and global quality standards. Operated independently under the technical expertise of Coolzer, Esgaming has built a reputation for delivering cutting-edge cases, power supplies, cooling fans, and gaming furniture to discerning users worldwide. All Esgaming products, including the Z300B, are manufactured under strict ISO9001-certified processes and comply with CE, UL, RoHS, and 80Plus international standards for performance, safety, and environmental sustainability. Esgaming products are now distributed worldwide and widely recognized for meeting international standards for performance, safety, and environmental sustainability. Explore Esgaming's full range of products: Website: Whatsapp: +86 13690469645 Email: sales05@ Media Contact Company Name: Esgaming (A Coolzer Brand) Contact Person: Sales Team Email: Send Email Phone: +86 13690469645 City: Shenzhen Country: China Website:
