Microsoft Completes Pre-Training of MAI-Base-2, a 2.8T-Parameter Model

Microsoft AI said on October 15, 2026 that it has completed pre-training MAI-Base-2, a mixture-of-experts foundation model with 80 billion active and 2.8 trillion total parameters.

Scale and efficiency

According to Microsoft’s lab note, MAI-Base-2 outperforms every open-weight model the team tested on held-out evaluations. The company estimates its training recipe is 4.2–12.3× more pre-training FLOPs-efficient than DeepSeek-V4-Pro-Base, which it calls the strongest open base model it compared against. Progress since MAI-Base-1 (announced in June 2026) is put at 6.3–12.7× FLOPs-efficiency improvement on the same ladder.

How it was trained

Microsoft trained MAI-Base-2 on 16,000 GB200 GPUs over 38 days in August and September 2026. The run averaged 21% model FLOPs utilization (MFU) and 89% goodput. The training mixture was about 50 trillion tokens, with a March 2026 knowledge cutoff for expanded web content. Unique training tokens grew roughly 36% versus MAI-Base-1.

Unlike MAI-Base-1, MAI-Base-2 is pre-trained natively with vision using an encoder-free VLM architecture. Microsoft says its VLM corpus contains 4.5 trillion unique tokens and, with repetition, vision data accounted for 18% of the 50T-token mixture.

Architecture notes

The company moved to MoE in every layer (MAI-Base-1 used alternating dense FFN and MoE layers), adopted the Muon optimizer at scale, and used activation gradient clipping for stabler training. Table figures in the post list MAI-Base-2 at 80B / 2.8T active/total parameters versus MAI-Base-1’s 35B / 1T.

What’s next

MAI-Base-2 is undergoing post-training. Microsoft says it will license model weights to trusted partners through Azure AI Foundry, and that preparations for MAI-Base-3 are already underway. The company stresses that pre-training is built on organic data without distillation from other models.