Microsoft plans to deploy a new generation of Maia 300 chips at gigawatt scale next year, with an expected performance per megawatt that is 40% higher than that of mainstream GPU systems. Microsoft will prioritize using its own chips to support internal AI services rather than renting out computing power to others, thereby reducing its dependence on NVIDIA GPU and lowering inference costs, and aiming to improve the profit margins of its AI business.
Microsoft is increasing its investment in self-developed AI chips, planning to deploy the Maia series of chips on a gigawatt scale, gradually reducing its dependence on NVIDIA GPU, and controlling the costs of AI infrastructure by improving inference efficiency.
On October 12th, according to Bloomberg, Microsoft announced on Monday that a new generation of Maia 300 chips will begin to be deployed in data centers next year, specifically designed for AI inference tasks. Microsoft expects that the number of token that can be processed per megawatt of Maia 300 will be 40% higher than that of mainstream GPU systems, which means that they will be able to handle more AI workloads with the same energy consumption.
Unlike Google and Amazon, which have made their self-developed chip computing power available to cloud customers, Microsoft currently has no intention of renting out the computing power of its Maia chips to external customers. This decision reflects Microsoft's priorities: to first use its self-developed chips to meet its own massive AI service needs, and then to transform the hardware efficiency advantages into business competitiveness.
Gigawatt-level deployment; energy efficiency advantages are the core logic.
Microsoft's increased investment in self-developed chips is essentially aimed at improving the inference efficiency of AI and reducing operating costs.
Inference refers to the use of trained AI models to perform actual tasks, such as summarizing meeting notes or answering research questions. It is also an important source of computing power demand during the operation of AI applications. Microsoft states that the Maia series is specifically designed for inference workloads in their own data centers, enabling more efficient execution of these tasks and reducing the need to purchase NVIDIA GPU. The latter remains the main source of computing power for Microsoft's inference business at present.
Rani Borkar, the person in charge of Microsoft's data center hardware business, stated: "We believe that we have created the best products for these workloads. Starting next year, we will deploy Maia at the gigawatt level to support this confidence with concrete actions."
The previous generation of products has been implemented in many places, and the new model will take over next year.
Microsoft's in-house chip strategy was launched a long time ago. At the beginning of this year, the company began installing the previous generation product Maia 200 in data centers. Currently, this chip is being used to power Copilot and other AI software, as well as for other tasks.
Borkar Introduction: Maia 200 can support OpenAI models, Microsoft's self-developed AI models, as well as multiple open-source model series. Its deployment scope covers data centers in the United States, Europe, and Australia, providing AI capability support for products such as Microsoft Teams and Copilot.
The new generation of Maia 300 is expected to be deployed next year. Microsoft hopes to leverage its energy efficiency advantages to further optimize the computing power configuration in its continuously expanding AI business and reduce inference costs.
In the field of self-developed chips, Microsoft is catching up with Google and Amazon.
Against the backdrop of cloud computing giants developing their own AI chips, Microsoft's approach is relatively lagging behind. Google and Amazon started earlier and have established a first-mover advantage in the field of self-developed chips, offering the related computing power to their cloud customers for use.
Microsoft, on the other hand, chose a different business path. Borkar states: "Our own AI service business is already large enough, and we prefer to serve ourselves first."
This means that, at present, Microsoft is more inclined to use the Maia chips for its internal AI operations, rather than generating revenue directly by leasing computing power. For investors, the commercial value of Maia will depend more on its ability to improve the operational efficiency of AI services, reduce inference costs, and ultimately enhance the profit margins of related businesses, rather than on how much direct revenue the chip leasing business can bring.
