Microsoft unveils 2.8T-parameter MAI-Base-2 model
Microsoft announced on October 15 that it completed pre-training for MAI-Base-2, a mixture-of-experts foundation model with 2.8 trillion total parameters and 80 billion active parameters. It was trained on 50 trillion tokens, including text and visual data, using 16,000 Nvidia GB200 GPUs for 38 days. Microsoft says the model outperformed the open-weight models included in its internal evaluations and estimates its training approach was 4.2 to 12.3 times more compute-efficient than DeepSeek-V4-Pro-Base when matching performance on selected tests. The company has not published equivalent comparisons against every leading proprietary model, and its efficiency figures estimate the compute required for comparable performance rather than showing universal superiority.
MAI-Base-2 advances Microsoft’s effort to build more of its own AI technology. Microsoft plans to license the model’s weights to trusted partners through Microsoft Foundry rather than release them publicly, a strategy aimed at giving enterprise customers a more predictable route to deploying customized AI. The model remains in post-training, with no announced pricing or general availability.
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Microsoft is building a much larger AI model of its own, but the company says its latest breakthrough is about using computing power more efficiently.
The company on Oct. 15 announced that it has completed pre-training MAI-Base-2, a mixture-of-experts (MoE) foundation model with 2.8 trillion total parameters and 80 billion active parameters. The model succeeds MAI-Base-1, which Microsoft introduced earlier this year.
According to Microsoft’s technical announcement, MAI-Base-2 outperformed the open-weight models included in its internal evaluations. The company estimates its training approach is 4.2 to 12.3 times more compute-efficient than DeepSeek-V4-Pro-Base when matching performance on selected tests.
Microsoft trained the model using 16,000 Nvidia GB200 GPUs over 38 days in August and September. Its training run processed 50 trillion tokens, including text and visual data.
MAI-Base-2 activates only a fraction of its parameters for each input, allowing it to retain a much larger overall model without using every parameter for every operation.
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