# On the Shifting Global Compute Landscape

[Hugging Face](https://yomu.fyi/company/hugging-face) · Tiezhen WANG, Irene Solaiman · Oct 29, 2025

**Type:** Explainer

## Summary

United States export controls on advanced artificial intelligence hardware have catalyzed rapid expansion across China's domestic silicon and open-weight model ecosystem. Faced with restricted access to high-end NVIDIA GPUs, Chinese developers accelerated deployments on domestic accelerators, including Huawei Ascend, Cambricon, and Baidu Kunlun. Hardware scarcity spurred architectural and algorithmic innovations in compute efficiency, notably DeepSeek's Multi-head Latent Attention and Group Relative Policy Optimization, alongside substantial post-training cost reductions. Organizations such as Baidu and Ant Group now train foundation models directly on domestic hardware, fostering non-CUDA software stacks and lowering inference costs globally. Consequently, the global artificial intelligence infrastructure is shifting from an exclusively American-focused paradigm toward a dual-ecosystem landscape powered by domestic chips and open-weight architectures.

## Context

U.S. export controls beginning in October 2022 restricted Chinese access to advanced NVIDIA GPUs like the A100 and H100 to protect national security. These restrictions, along with updated performance density caps targeting modified chips like the A800 and H800, created compute scarcity for Chinese AI laboratories and threatened their access to high-end infrastructure.

## Approach / What changed

Chinese organizations responded by investing in domestic accelerators, such as Huawei Ascend, Cambricon, and Baidu Kunlun, while developing non-CUDA software alternatives. Simultaneously, AI labs prioritized algorithmic compute efficiency, developing techniques like Multi-head Latent Attention and Group Relative Policy Optimization to train and deploy open-weight models at lower hardware costs.

## Takeaways

- Compute constraints incentivized algorithmic optimizations such as DeepSeek's Multi-head Latent Attention (MLA) and Group Relative Policy Optimization (GRPO), lowering model post-training costs.
- Domestic Chinese chips, including Huawei's Ascend, Cambricon, and Baidu's Kunlun, have expanded from running model inference to powering foundation model training runs for companies like Baidu and Ant Group.
- U.S. regulatory policy progressed from bandwidth thresholds to Total Processing Performance (TPP) and performance density metrics, eventually prompting licensing rules and revenue-sharing mechanisms for modified chips.

**Tags:** [Architecture](https://yomu.fyi/topic/architecture), [LLMs](https://yomu.fyi/topic/llm), [Machine Learning](https://yomu.fyi/topic/machine-learning), [Open Source](https://yomu.fyi/topic/open-source)

[Read original post](https://huggingface.co/blog/huggingface/shifting-compute-landscape)