Microsoft는 3세대 자체 AI 칩(Maia 300으로 알려짐)을 공개했으며, 내년 배치 규모가 100만 개를 넘을 수 있다고 보도되었다.

Microsoft (MSFT) on Monday officially unveiled its third-generation in-house AI accelerator, the Maia 300, a chip purpose-built for AI inference workloads. The company plans to begin deployment at multi-gigawatt scale starting next year, with single-year deployment volumes potentially exceeding 1 million units. The move signals Microsoft is accelerating its push to reduce dependence on Nvidia (NVDA) GPUs, carving out its own path in the AI infrastructure race through system-level efficiency gains.

The Maia 300 is the third generation in Microsoft's in-house AI chip family. The first-generation Maia 100 debuted in 2023, while the second-generation Maia 200 was only formally announced earlier this year and is now supporting production workloads including Copilot, GitHub Copilot, Microsoft Foundry, and Microsoft's own AI models. Judging by the release cadence, Microsoft is advancing its custom silicon on a roughly one-generation-per-year cycle, and the arrival of the Maia 300 marks the point where this strategy enters a critical phase of scaled deployment.

**Chiplet Design and Efficiency Breakthroughs**

Architecturally, the Maia 300 takes a fundamentally different technical approach from Nvidia. Nvidia's AI accelerators have traditionally centered on a single large compute die surrounded by high-bandwidth memory. The Maia 300, by contrast, employs a chiplet design, splitting compute, memory controllers, and input/output functions into multiple dies integrated into a single package.

The core compute chiplets are fabricated on Taiwan Semiconductor Manufacturing's (2330.TW) most advanced 2-nanometer process (N2P node), while other chiplets handling data movement and transmission use the more cost-effective 3-nanometer process. This hybrid process strategy allows Microsoft to pursue maximum performance on critical compute units while controlling costs on auxiliary functions.

On the memory front, the Maia 300 packs eight stacks of HBM4 high-bandwidth memory, totaling 288GB of capacity with transfer bandwidth of 18TB per second. By comparison, the previous-generation Maia 200 featured 216GB of HBM3E memory with 7TB per second of bandwidth. The new chip delivers roughly 33% more memory capacity and 157% more bandwidth, a critical upgrade for modern AI workloads that require long-context processing and multi-step reasoning.

In terms of raw compute, the Maia 300 delivers up to 13.8 PFLOPS at FP4 precision and 6.9 PFLOPS at FP8. Per-chip power consumption is approximately 950 watts, up from the Maia 200's 750 watts, but Microsoft emphasizes that the real benefit comes from system-level optimization.

**40% Higher Output Per Megawatt**

Scott Guthrie, executive vice president of Microsoft's Cloud and AI group, said the Maia 300 processes approximately 40% more tokens per megawatt of power consumed than "leading GPU systems." In a fully loaded rack configuration with 96 accelerators, rack-level token throughput reaches seven times that of the Maia 200.

However, Microsoft did not specify which GPU system served as the comparison baseline. Nvidia's latest GB300 AI server rack packs 72 GPUs with power consumption of roughly 142 kilowatts, while the Maia 300's 96-accelerator rack draws approximately 167 kilowatts. With specification details of the comparison target not fully disclosed, outside observers have difficulty independently verifying the actual substance of the claimed 40% efficiency advantage.

Rani Borkar, corporate vice president of Azure Hardware Systems and Infrastructure, told reporters: "We believe we have built the best product for these workloads. We will back that confidence with action by deploying Maia at gigawatt scale starting next year." She added that through holistic system optimization, Microsoft aims to ensure every watt of power, every dollar invested, and every byte of memory supports more AI compute.

**Prioritizing Internal Services, Not External Rental**

Unlike Google and Amazon, which have opened their in-house chip compute capacity to cloud customers, Microsoft has no current plans to rent out Maia compute directly to external clients. Borkar was blunt: "Our own AI services business is large enough. We want to serve ourselves first."

The logic of this approach is straightforward: Microsoft's internal AI service compute demands are enormous. From Copilot to Azure OpenAI Service, every offering requires massive inference capacity. If in-house chips can satisfy this demand, the savings on GPU procurement and operating costs flow directly to AI business margins. For investors, Maia's commercial value lies not in how much direct revenue chip rental can generate, but in whether it can effectively lower Microsoft's per-unit AI infrastructure costs.

Microsoft's capital expenditure this year is expected to exceed $175 billion (approximately NT$5.6 trillion), with the vast majority directed toward AI infrastructure. At this scale of spending, maximizing compute efficiency per dollar has become a core concern for both Microsoft management and investors. The large-scale deployment of the Maia 300 is one of Microsoft's answers to that challenge.

**The Co-opetition Dynamic with Nvidia**

Microsoft stressed that expanding Maia deployment is "not about cutting other vendors' products, but about raising the overall processing capacity of the infrastructure." Andrew Wall, a business lead on the project, said in an interview that Maia's expansion "is not about replacing Nvidia products, but about increasing the overall compute capacity of the infrastructure." This framing has practical considerations: Microsoft remains one of Nvidia's largest customers, and the two companies' collaboration on AI infrastructure will not change in the near term.

From an industry trend perspective, Microsoft, Google, Amazon, and AI developers like OpenAI have all accelerated investment in custom silicon recently, all aiming to gain more bargaining power and technological autonomy in an AI compute market dominated by Nvidia. OpenAI recently unveiled its own chip, Jalapeno, signaling that AI model developers are also integrating downstream.

Mustafa Suleyman, CEO of Microsoft AI, noted: "We have to work across the full stack, all the way down to the silicon level. At this scale, we need a platform we own and can co-design with our own models. That's why running Microsoft AI models on Maia chips is a critical focus for our team." These remarks reveal the ultimate goal of Microsoft's custom silicon strategy: not merely hardware substitution, but hardware-software co-design, building a fully autonomous AI value chain from silicon to models to services.

Maia 300 deployment will begin in the United States next year before gradually expanding to data centers worldwide, including Japan. Microsoft's goal is to make Maia the "primary AI inference system" for its own products and services, meaning the compute architecture behind Microsoft's AI services will undergo a fundamental transformation in the coming years.