Microsoft

Microsoft came to custom silicon later than Google or Amazon and for a sharper reason: it had committed to serving OpenAI models at a scale that made its NVIDIA bill a strategic problem rather than a line item.

Maia is explicitly an inference chip. That is the honest place for a first-generation: training a frontier model demands a mature software stack, while serving one is a more tractable engineering problem with far larger aggregate volume.

Like every hyperscaler part, it is captive. Microsoft designs chips to reduce what it pays for chips, not to enter the market for them.

Silicon from Microsoft

3 chips on record, newest first.

  1. 2026

    Microsoft Maia 300

    TSMC N2P (compute chiplets) Inference accelerator, eight HBM4 stacks, 950 W TDP, 96 per rack

    up to 2.5x more performance per watt compared to Maia 200

    Relative only
  2. 2026

    Microsoft Maia 200

    TSMC 3nm Inference SoC, 272 MB on-chip SRAM, 750 W

    over 10 petaFLOPS in 4-bit precision (FP4) per chip

    Absolute
  3. 2023

    Microsoft Maia 100

    TSMC 5nm First-gen Azure AI accelerator, about 820 mm2, 500 MB of L1/L2

    Peak Dense Tensor POPS 6bit: 3, 9bit: 1.5, BF16: 0.8

    Absolute

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