微软推出 Maia 300 芯片并将其定为旗下产品主要推理系统
微软宣布自研 Maia 芯片将成为旗下产品主要推理系统，并推出 Maia 300
微软宣布自 2027 年起将 Maia 芯片家族扩展至多吉瓦部署，使其成为旗下产品和服务的主要推理系统。新推出的 Maia 300 采用多芯片粒设计，官方称其每瓦 token 产出比主流 GPU 系统高 40%，旨在优化长上下文推理及智能体任务的能效与吞吐量。
微软宣布将自研 Maia 芯片作为主要推理系统并推出 Maia 300，该举措展示了大型科技公司在降低对外部硬件依赖方面的战略调整。
Rohan Paul· @rohanpaul_ai · X·2026-10-12 21:52· 24 分钟前AI 评分73
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Satya Nadella 宣布微软从下一个日历年起将 Maia 芯片家族扩展到多吉瓦部署，使其成为自家产品和服务的主要推理系统，并推出 Maia 300，官方称其每瓦 token 产出比主流 GPU 系统高 40%。
正文
Satya Nadella officially announced that Microsoft will make its in-house Maia chips the primary inference system for its products, scaling to multiple gigawatts.
This will make it less reliant on NVIDIA hardware, and Maia 300 is meant to replace AMD chips too, not only Nvidia. The key claim is 40% more tokens per megawatt than leading GPU-based systems
A full rack packs 96 accelerators into 167kW with 27.6TB of HBM, and Microsoft claims it delivers up to 7x the token throughput of a Maia 200 rack.
The shift will start in 2027 with Maia 300, a third-generation accelerator built for long-context reasoning, low-latency chat and multi-step agent tasks.
Its predecessor, Maia 200, already serves production traffic including Microsoft Copilot and Microsoft claims Maia 200 produces up to 1.7x more tokens per watt than leading GPU infrastructure.
Maia 300 goes after the cost of moving data, which Microsoft says now limits large inference systems more than raw arithmetic does.
Maia 300 uses a “multi-chiplet” design, A traditional chip is one large slab of silicon that does everything: the math (compute), managing the memory (memory controller) and talking to other chips (I/O). Maia 300 instead splits those jobs into several smaller pieces of silicon, called chiplets, packaged together so they act as one chip. That leaves more room on the compute chiplets for the parts that actually run the AI math, and each chiplet can be made with whatever manufacturing process suits its job best.
Each chip pairs 8 HBM4 stacks delivering 18.0 TB/s of peak bandwidth with a 950W TDP, and uses on-die SRAM as a scratchpad to relieve that memory.
Support for low-precision formats such as an enhanced MXFP4 shrinks the KV cache, the stored attention state that grows with every token, so longer contexts fit within the same power budget.
Supply is the harder constraint, since Microsoft has reportedly been negotiating with TSMC for more than 300,000 Maia 300 units for 2027 delivery, a figure still under negotiation rather than a signed purchase order.
Today we are announcing our biggest bet yet on our own silicon. Starting next calendar year, we will scale our Maia family to multi-gigawatt deployments, making it the primary inference system for our own product and services. And we’re introducing Maia 300, which delivers 40% more tokens per watt compared to leading GPU-based systems. We are already deploying Maia 200 across the US, Europe, and Australia, delivering up to 1.7x more tokens per watt across experiences in Teams and Copilot. Our goal is to build on the full-stack efficiency gains we’ve already seen, while continuing to bring the latest innovations from across the ecosystem into our fleet.在 X 查看被引用的帖子
来源：Rohan Paul · x.com
来源：AIHot · aihot.news