<p>Microsoft has unveiled its Maia 300 artificial intelligence chip, marking another step in its effort to build the computing infrastructure behind its AI services. The company plans to begin multi-gigawatt deployments next year, potentially using more than one million chips to support workloads such as coding and complex business tasks. The announcement comes as technology companies develop their own processors to reduce reliance on Nvidia, whose graphics processing units dominate much of the AI computing market. Microsoft says Maia 300 is designed to improve computing efficiency by optimising the chip, memory and wider server system together.</p><h2><strong>What is Microsoft Maia 300, and what makes it different?</strong></h2><p>Maia 300 is the third generation of Microsoft's custom AI chips. Its predecessor already supports Microsoft's internal AI models and models from OpenAI delivered through the Azure cloud platform.</p><p>Rather than using one large computing processor, Microsoft's new design combines smaller specialised chips, known as chiplets. The main computing chiplets use 2-nanometre manufacturing technology from Taiwan Semiconductor Manufacturing Company (TSMC), while other components use 3nm technology for tasks such as moving data around the system.</p><p>This approach allows Microsoft to use different manufacturing technologies for different functions instead of building every part of the processor in the same way.</p><h2>Microsoft claims 40% more computing efficiency</h2><p>Microsoft says Maia 300 can deliver around 40% more useful computing per megawatt than leading GPU-based systems. The company did not identify the systems used for the comparison, so the figure should be treated as Microsoft's claim rather than an independently verified result.</p><p>The chips will be installed in server racks containing 96 processors. Each rack is expected to use about 167 kilowatts of power.</p><p>For comparison, Nvidia's latest GB300 AI server racks contain 72 GPUs and use around 142 kilowatts, according to the supplied information. The different rack configurations underline why performance and power consumption need to be assessed together, rather than by chip count alone.</p><h2>Why Microsoft is challenging Nvidia</h2><p>Demand for AI computing is driving technology companies to develop custom processors tailored to their own workloads. Microsoft's Maia 300 is intended to power its internal services and cloud infrastructure while improving the amount of computing delivered for each unit of electricity.</p><p>The scale of its planned rollout reflects the investment required. Microsoft expects to spend more than $175 billion in capital expenditure this calendar year.</p><p>However, Maia 300's commercial impact will depend on real-world performance, availability and operating costs. Its deployment could strengthen Microsoft's control over its AI infrastructure, but it does not mean Nvidia's position in the market is immediately under threat.</p>

Microsoft has unveiled its Maia 300 AI chip and plans multi-gigawatt deployments next year, potentially involving more than one million units. The company claims 40% more useful computing per megawatt than leading GPU systems as it expands its AI infrastructure.

Microsoft has unveiled its Maia 300 artificial intelligence chip, marking another step in its effort to build the computing infrastructure behind its AI services. The company plans to begin multi-gigawatt deployments next year, potentially using more than one million chips to support workloads such as coding and complex business tasks. The announcement comes as technology companies develop their own processors to reduce reliance on Nvidia, whose graphics processing units dominate much of the AI computing market. Microsoft says Maia 300 is designed to improve computing efficiency by optimising the chip, memory and wider server system together. What is Microsoft Maia 300, and what makes it different? Maia 300 is the third generation of Microsoft's custom AI chips. Its predecessor already supports Microsoft's internal AI models and models from OpenAI delivered through the Azure cloud platform. Rather than using one large computing processor, Microsoft's new design combines smaller specialised chips, known as chiplets. The main computing chiplets use 2-nanometre manufacturing technology from Taiwan Semiconductor Manufacturing Company (TSMC), while other components use 3nm technology for tasks such as moving data around the system. This approach allows Microsoft to use different manufacturing technologies for different functions instead of building every part of the processor in the same way. Microsoft claims 40% more computing efficiency Microsoft says Maia 300 can deliver around 40% more useful computing per megawatt than leading GPU-based systems. The company did not identify the systems used for the comparison, so the figure should be treated as Microsoft's claim rather than an independently verified result. The chips will be installed in server racks containing 96 processors. Each rack is expected to use about 167 kilowatts of power. For comparison, Nvidia's latest GB300 AI server racks contain 72 GPUs and use around 142 kilowatts, according to the supplied information. The different rack configurations underline why performance and power consumption need to be assessed together, rather than by chip count alone. Why Microsoft is challenging Nvidia Demand for AI computing is driving technology companies to develop custom processors tailored to their own workloads. Microsoft's Maia 300 is intended to power its internal services and cloud infrastructure while improving the amount of computing delivered for each unit of electricity. The scale of its planned rollout reflects the investment required. Microsoft expects to spend more than $175 billion in capital expenditure this calendar year. However, Maia 300's commercial impact will depend on real-world performance, availability and operating costs. Its deployment could strengthen Microsoft's control over its AI infrastructure, but it does not mean Nvidia's position in the market is immediately under threat.

<p>Microsoft has unveiled its Maia 300 AI chip and plans multi-gigawatt deployments next year, potentially involving more than one million units. The company claims 40% more useful computing per megawatt than leading GPU systems as it expands its AI infrastructure.</p>

Microsoft’s new AI chip uses 2nm technology to challenge Nvidia’s dominance

<p>Apple could be preparing one of its biggest hardware shifts in years: a return to the enterprise server market, this time with AI at the centre of the strategy.</p><p>&nbsp;</p><p>According to a report by The Information, Apple is developing an AI-focused server that could use two or four future M8 Ultra chips. The machine is reportedly aimed at AI developers, businesses and governments, with a possible launch in 2029. Apple has not confirmed the project, and it could still be cancelled or changed.</p><p>The reported move comes as AI companies are increasingly buying Apple's high-performance Macs to run AI workloads.</p><h2><strong>Apple’s reported M8 Ultra AI server</strong></h2><p>The proposed server would reportedly come in two configurations. One could combine two M8 Ultra chips, while a larger version could use four. The focus would be on AI inference essentially running trained AI models and generating responses rather than primarily training huge models. The project reportedly began around a year ago, when John Ternus was Apple's hardware engineering chief. Ternus became Apple CEO on September 1. The timing is significant. AI companies have reportedly been buying Mac mini and Mac Studio machines in large numbers. The Information reported that OpenAI had bought tens of thousands of them, while Anthropic has rented Mac minis through Amazon Web Services.</p><p>&nbsp;</p><h2><strong>Why Nvidia could be part of Apple’s plan</strong></h2><p>The most surprising part of the report is that Apple is considering Nvidia's NVLink Fusion to connect its own M8 Ultra chips. NVLink Fusion includes hardware and software designed to allow processors to communicate at very high speeds inside data centres. Apple reportedly believes Nvidia's technology could offer the connectivity needed to scale multiple chips inside an AI server. The possibility is notable because Apple and Nvidia have had a strained relationship for years. Their reported discussions therefore suggest that AI infrastructure could be creating new areas of cooperation. However, Nvidia's involvement is not confirmed. Apple could proceed without the technology.</p><p>&nbsp;</p><h2><strong>Apple’s return to servers</strong></h2><p>If launched, the machine would represent Apple's return to dedicated enterprise servers after it discontinued its Xserve line in 2011. Apple already operates servers for its Private Cloud Compute system, but those are not products sold to outside organisations. The reported M8 Ultra machine would be different: Apple would potentially sell it directly to AI developers, businesses and governments. There is also a major challenge. Enterprise customers need specialised software, remote management, developer support and long-term service. Apple would have to build much more than the hardware itself. For now, the M8 Ultra server remains a reported project rather than an announced Apple product. But if it reaches the market in 2029, Apple could find itself competing in an AI server industry dominated by companies such as Nvidia using its own silicon, and potentially Nvidia technology, to do it.</p>

Apple is reportedly developing an enterprise AI server powered by future M8 Ultra chips, potentially marking its return to the server market after discontinuing Xserve in 2011. Nvidia’s NVLink Fusion technology is also being considered.

Apple could be preparing one of its biggest hardware shifts in years: a return to the enterprise server market, this time with AI at the centre of the strategy. According to a report by The Information, Apple is developing an AI-focused server that could use two or four future M8 Ultra chips. The machine is reportedly aimed at AI developers, businesses and governments, with a possible launch in 2029. Apple has not confirmed the project, and it could still be cancelled or changed. The reported move comes as AI companies are increasingly buying Apple's high-performance Macs to run AI workloads. Apple’s reported M8 Ultra AI server The proposed server would reportedly come in two configurations. One could combine two M8 Ultra chips, while a larger version could use four. The focus would be on AI inference essentially running trained AI models and generating responses rather than primarily training huge models. The project reportedly began around a year ago, when John Ternus was Apple's hardware engineering chief. Ternus became Apple CEO on September 1. The timing is significant. AI companies have reportedly been buying Mac mini and Mac Studio machines in large numbers. The Information reported that OpenAI had bought tens of thousands of them, while Anthropic has rented Mac minis through Amazon Web Services. Why Nvidia could be part of Apple’s plan The most surprising part of the report is that Apple is considering Nvidia's NVLink Fusion to connect its own M8 Ultra chips. NVLink Fusion includes hardware and software designed to allow processors to communicate at very high speeds inside data centres. Apple reportedly believes Nvidia's technology could offer the connectivity needed to scale multiple chips inside an AI server. The possibility is notable because Apple and Nvidia have had a strained relationship for years. Their reported discussions therefore suggest that AI infrastructure could be creating new areas of cooperation. However, Nvidia's involvement is not confirmed. Apple could proceed without the technology. Apple’s return to servers If launched, the machine would represent Apple's return to dedicated enterprise servers after it discontinued its Xserve line in 2011. Apple already operates servers for its Private Cloud Compute system, but those are not products sold to outside organisations. The reported M8 Ultra machine would be different: Apple would potentially sell it directly to AI developers, businesses and governments. There is also a major challenge. Enterprise customers need specialised software, remote management, developer support and long-term service. Apple would have to build much more than the hardware itself. For now, the M8 Ultra server remains a reported project rather than an announced Apple product. But if it reaches the market in 2029, Apple could find itself competing in an AI server industry dominated by companies such as Nvidia using its own silicon, and potentially Nvidia technology, to do it.

<p>Apple is reportedly developing an enterprise AI server powered by future M8 Ultra chips, potentially marking its return to the server market after discontinuing Xserve in 2011. Nvidia’s NVLink Fusion technology is also being considered.</p>

Apple reportedly building AI server with M8 Ultra chips, may use Nvidia technology

<p>Nvidia is becoming much more than the company behind the world’s most powerful AI chips.</p><p>&nbsp;</p><p>The chip giant’s equity investments have surged to $99 billion, up from about $7 billion a year earlier and roughly $2.2 billion two years ago, according to its latest regulatory filing. Nvidia also disclosed another $25 billion in equity investment commitments as of July 26. The rapid expansion shows how Nvidia is using its enormous financial strength to shape the AI industry around its hardware. Instead of simply selling GPUs, Nvidia is increasingly putting money into the companies that need those GPUs.</p><h2>Where is Nvidia putting its billions?</h2><p>A large part of the strategy is focused on frontier AI labs.</p><p>&nbsp;</p><p>Nvidia CFO Colette Kress said the company had invested nearly $50 billion in frontier AI labs. Nvidia announced a $30 billion investment in OpenAI in February as part of OpenAI’s $110 billion funding round.<br><br>Nvidia is also backing the rapidly growing group of so-called neoclouds companies that buy large numbers of GPUs and rent computing power to AI customers. It invested $2 billion in CoreWeave in January and another $2 billion in Nebius in March. Nvidia said the Nebius investment would help accelerate its build-out of a full-stack AI cloud platform. The strategy has now expanded beyond cloud and AI models.</p><h2><strong>Nvidia is betting on the technology behind AI</strong></h2><p>Nvidia has also been investing heavily in optical and photonics technology, which uses light to move data rather than traditional electrical connections. Companies including Lumentum, Coherent and Marvell have each received major investments, according to CNBC’s analysis. The technology could become increasingly important as AI systems require faster movement of enormous amounts of data. Nvidia has also benefited from the rising value of some of its investments. Its $5 billion Intel investment has reportedly risen to around $30 billion, while its SpaceX holding was valued at about $21 billion as of June, according to CNBC.</p><h2><strong>Why Nvidia is doing this</strong></h2><p>The strategy is simple: help the AI ecosystem grow, and Nvidia can grow with it. Nvidia itself says its investments are intended to enhance growth opportunities, cultivate its ecosystem and strengthen its competitive position.</p><p>&nbsp;</p><p>That creates a powerful cycle. AI companies need computing power. Nvidia supplies the GPUs. By investing in those companies, Nvidia can also help them raise the capital needed to buy more computing infrastructure.</p><p>The result is a company that increasingly sits at several levels of the AI economy supplier, investor, infrastructure partner and, increasingly, owner. Nvidia’s $99 billion portfolio shows just how much that role has expanded.</p>

Nvidia’s equity investments have surged to $99 billion from about $7 billion a year earlier. The chip giant is backing AI labs, cloud providers, infrastructure and emerging technologies, using its capital to strengthen the ecosystem around its GPUs.



Nvidia is becoming much more than the company behind the world’s most powerful AI chips. The chip giant’s equity investments have surged to $99 billion, up from about $7 billion a year earlier and roughly $2.2 billion two years ago, according to its latest regulatory filing. Nvidia also disclosed another $25 billion in equity investment commitments as of July 26. The rapid expansion shows how Nvidia is using its enormous financial strength to shape the AI industry around its hardware. Instead of simply selling GPUs, Nvidia is increasingly putting money into the companies that need those GPUs. Where is Nvidia putting its billions? A large part of the strategy is focused on frontier AI labs. Nvidia CFO Colette Kress said the company had invested nearly $50 billion in frontier AI labs. Nvidia announced a $30 billion investment in OpenAI in February as part of OpenAI’s $110 billion funding round. Nvidia is also backing the rapidly growing group of so-called neoclouds companies that buy large numbers of GPUs and rent computing power to AI customers. It invested $2 billion in CoreWeave in January and another $2 billion in Nebius in March. Nvidia said the Nebius investment would help accelerate its build-out of a full-stack AI cloud platform. The strategy has now expanded beyond cloud and AI models. Nvidia is betting on the technology behind AI Nvidia has also been investing heavily in optical and photonics technology, which uses light to move data rather than traditional electrical connections. Companies including Lumentum, Coherent and Marvell have each received major investments, according to CNBC’s analysis. The technology could become increasingly important as AI systems require faster movement of enormous amounts of data. Nvidia has also benefited from the rising value of some of its investments. Its $5 billion Intel investment has reportedly risen to around $30 billion, while its SpaceX holding was valued at about $21 billion as of June, according to CNBC. Why Nvidia is doing this The strategy is simple: help the AI ecosystem grow, and Nvidia can grow with it. Nvidia itself says its investments are intended to enhance growth opportunities, cultivate its ecosystem and strengthen its competitive position. That creates a powerful cycle. AI companies need computing power. Nvidia supplies the GPUs. By investing in those companies, Nvidia can also help them raise the capital needed to buy more computing infrastructure. The result is a company that increasingly sits at several levels of the AI economy supplier, investor, infrastructure partner and, increasingly, owner. Nvidia’s $99 billion portfolio shows just how much that role has expanded.

<p>Nvidia’s equity investments have surged to $99 billion from about $7 billion a year earlier. The chip giant is backing AI labs, cloud providers, infrastructure and emerging technologies, using its capital to strengthen the ecosystem around its GPUs.</p>

Nvidia now has $99 billion invested across the AI industry, here’s where the money is going

Powered by the new RTX Spark superchip, Microsoft's 15-inch Surface Laptop Ultra features 128GB of unified memory and 1 petaflop of AI compute to run massive 120-billion parameter models locally.


<p>Powered by the new RTX Spark superchip, Microsoft's 15-inch Surface Laptop Ultra features 128GB of unified memory and 1 petaflop of AI compute to run massive 120-billion parameter models locally.</p>

‘One chip, massive impact’: Nvidia's vision for the future of PCs

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Microsoft’s new AI chip uses 2nm technology to challenge Nvidia’s dominance

Apple’s big AI hardware move: M8 Ultra server could arrive in 2029

From OpenAI to Intel: Where Nvidia is putting its billions

‘One chip, massive impact’: Nvidia's vision for the future of PCs