Microsoft, Tsinghua use Nvidia chip to train AI without real data

Tsinghua University and Microsoft researchers developed a synthetic data pipeline called SynthSmith to train AI models without relying on real-world data, using Nvidia chips for computation.

Using only synthetic data, the team trained a 7-billion-parameter coding model that outperformed larger 14-billion-parameter models on benchmarks.

The experiment used 128 Nvidia H20 chips for 220 hours during supervised fine-tuning and 32 H200 chips for seven days during reinforcement learning.

The researchers said computational limits prevent scaling the approach to models above 100 billion parameters.

They open-sourced the research so others can build on it without high training costs, using Nvidia H20 and H200 chips that are available to Chinese AI firms under export controls.

🔗 Source: South China Morning Post

🧠 Food for thought

Implications, context, and why it matters.

Key details on performance and release plans need to be checked to validate the research

Clarification is required for the claim that the research is “open-sourcing.”
Code for preparing the training material is posted on GitHub, while the model weights (the trained model parameters needed to run the model) are planned for release without a timeline 1.
Licensing terms for the code and the eventual model weights, which set the rules for commercial use, must be confirmed.
Performance results should be reviewed beyond broad statements about beating larger models.
On LiveCodeBench v5, an average pass rate of 62.9 on eight attempts (avg@8) was reported, while v6 was put at 55.8, ahead of DeepCoder-14B-Preview and AReal-boba2-14B 1.

China-based GPU cloud providers could treat synthetic-data training as a service opportunity, but export-policy and demand claims need tighter sourcing

China-based GPU cloud providers could package this synthetic-data training workflow as a paid service for labs and small teams.
The experiment ran on Nvidia H20 and H200 chips, with 128 H20 chips used for supervised fine-tuning and 32 H200 chips used for reinforcement learning [News Article].
The Trump administration made H20 and H200 chips available to Chinese companies after lobbying by Nvidia to lift export controls [News Article].
The authors flagged compute limits as a scaling barrier, so Alibaba Cloud and Tencent Cloud could sell higher-capacity setups to groups trying to replicate or expand the method [News Article].
Any demand figure of “over 400,000 H200 units” tied to Alibaba and ByteDance needs clear attribution since it is not supported by the provided source set 2.