# Microsoft MAI-Base-2 completes pre-training at 2.8T params

URL: https://techaiwire.com/articles/microsoft-mai-base-2-pre-training-2-8-trillion-parameters/

_By Claire Whitmore - Oct 16, 2026_

**By the numbers:**

- 2.8T - total parameters, with 80B active per token
- 16,000 - Nvidia GB200 GPUs used for training
- 38 days - of pre-training in August and September 2026
- 4.2-12.3x - claimed compute efficiency versus DeepSeek-V4-Pro-Base

Microsoft AI has finished pre-training MAI-Base-2, its next in-house foundation model, the company said on October 15, 2026. The model has 2.8 trillion parameters, with 80 billion active for each token. Microsoft says it beats every open-weight model it tested and took far less computing power than a leading rival. It shows Microsoft building frontier-scale models of its own, alongside the OpenAI models it already offers.

Pre-training is the first and most expensive stage of building a language model. The model reads trillions of words and learns to predict the next one. MAI-Base-2 is now in post-training, the stage that turns a raw model into a useful assistant. It is not available to use yet.

## What Microsoft built

MAI-Base-2 is a mixture-of-experts (MoE) model. Only a small part of the network, the "experts" picked for each token, runs at any moment. That is why a 2.8-trillion-parameter model needs only 80 billion parameters of work per token.

| Item | MAI-Base-2 |
|---|---|
| Total parameters | 2.8 trillion |
| Active parameters per token | 80 billion |
| Training hardware | About 16,000 Nvidia GB200 GPUs |
| Training time | 38 days, in August and September 2026 |
| Training mix | 50 trillion tokens |
| Image-and-text share | 18%, from 4.5 trillion unique tokens |
| Status | In post-training, no release date |

The model learned from images as well as text. Image-and-text data made up 18% of the 50-trillion-token mix, according to [Microsoft AI's post](https://microsoft.ai/news/frontier-pre-training-at-microsoft-ai/). The number of unique training tokens grew by roughly 36% over its predecessor, MAI-Base-1.

## How well the training ran

Microsoft shared two measures of efficiency. Model FLOPs utilization, or MFU, averaged 21%. It is the share of the GPUs' peak math power that went into useful training. Goodput was 89%. That is the share of time spent training, as opposed to recovering from crashes and restarts.

Microsoft also took a public position on a sensitive topic. "Our pre-training is built on organic data which does not involve distillation from other models," its post says. Distillation means training a model on another model's answers. This week, Anthropic described [new defenses against distillation of Claude](/articles/anthropic-claude-distillation-defenses-90-99-percent-api-changes/).

## The efficiency claim

The headline figure is a comparison with DeepSeek-V4-Pro-Base, a leading open model. Microsoft says MAI-Base-2 reaches the same results with 4 to 12 times fewer training FLOPs. The body of the post gives the range as 4.2 to 12.3 times.

"MAI-Base-2 outperforms every open-weight model we tested on our held-out evaluations," Microsoft wrote. Held-out evaluations are tests kept out of the training data. These are Microsoft's own tests, and no outside group has checked them.

## Where it fits

Microsoft launched its MAI program publicly on June 2, 2026, at its Build conference, with seven models, [Crypto Briefing reports](https://cryptobriefing.com/microsoft-completes-pre-training-mai-base-2/). The team is Microsoft AI, led by Mustafa Suleyman.

The step up in scale is large. MAI-Base-1 was trained on 8,192 GB200 GPUs and 30 trillion tokens of human-written data, plus 3.55 trillion tokens in a later stage. MAI-Thinking-1, built on that base, scored about 52.8% on SWE-Bench Pro, a coding test, Crypto Briefing says.

Microsoft is also building its own AI chips. It plans multi-gigawatt deployments of Maia chips from 2027, according to Crypto Briefing. We covered the [Maia 300 chip](/articles/microsoft-maia-300-ai-chip-inference-hbm4-2027/) this week. Work on MAI-Base-3 has already started, Microsoft says.

## What this means for developers

There is nothing to call or download today. MAI-Base-2 is a base model in post-training, and Microsoft has given no release date or price.

Watch for two things when it ships. First, check whether the efficiency edge shows up as lower prices on Microsoft's platforms. A model that costs less to train can cost less to serve, but Microsoft has promised nothing. Second, wait for outside benchmarks. "Every open-weight model we tested" depends on which models and which tests.

For teams building on Azure, the bigger point is choice. A Microsoft-trained frontier model would sit next to OpenAI's models on the same platform. If you have tied your code to one model family, keep a thin layer between your app and the model. That way you can compare MAI models against your current choice when they arrive.

## Sources

- [Frontier Pre-training at Microsoft AI](https://microsoft.ai/news/frontier-pre-training-at-microsoft-ai/) - Microsoft AI
- [Microsoft finishes pre-training MAI-Base-2, its next in-house foundation model](https://cryptobriefing.com/microsoft-completes-pre-training-mai-base-2/) - Crypto Briefing