On the Shifting Global Compute Landscape

The status quo of AI chip usage, that was once almost entirely U.S.-based, is changing. China’s immense progress in open-weight AI development is now being met with rapid domestic AI chip development. In the past few months, highly performant open-weight AI models’ inference in China has started to be powered by chips such as Huawei’s Ascend and Cambricon, with some models starting to be trained using domestic chips.

There are two large implications for policymakers and AI researchers and developers respectively: U.S. export controls correlates with expedited Chinese chip production, and chip scarcity in China likely incentivized many of the innovations that are open-sourced and shaping global AI development.

China’s chip development correlates highly with stronger export controls from the U.S. Under uncertainty of chip access, Chinese companies have innovated with both chip production and algorithmic advances for compute efficiency in models. Out of necessity, decreased reliance on NVIDIA has led to domestic full stack AI deployments, as seen with Alibaba.

Architectural Exploration: Grassroots independent researchers such as Peng Bo , have championed Linear Attention as a potential successor to the Transformer. This approach, sometimes dubbed the "revenge of the RNN" and seen in models like RWKV , has been scaled into commercial grade models like MiniMax M1 and Qwen-Next by Chinese labs who willingly bet on high-risk, high-reward research. Meanwhile, DeepSeek has taken a different path by iterating on the original Transformer architecture. Their work introduces innovations like Multi-head Latent Attention (MLA ) and DeepSeek Sparse Attention (DSA) introduced with its v3.2 model, which are designed to significantly reduce computational costs during inference without sacrificing performance, while also accelerating Reinforcement Learning (RL) exploration through faster rollouts. Highly performant proprietary models architectures are not public and are therefore difficult to compare.

Chinese labs took a different path, focusing on architectural efficiency and open collaboration. Open source, once a niche interest, became the new norm, a pragmatic choice for rapidly accelerating progress through shared knowledge. This paradigm allows organizations to leverage existing, high-quality pre-trained models as a foundation for specialised applications through post-training, dramatically reducing the compute burden. A primary example is the DeepSeek R1 model, which required less than $300,000 for post-training on its V3 architecture, thereby lowering the barrier for companies to develop sophisticated models. While not the full base model, the cost reduction for the reasoning model is substantial. Algorithmic advances that improve memory such as Multi-head Latent Attention (MLA) with DeepSeek’s V3 model , likely incentivized by compute limitations, are a large part of January 2025’s “DeepSeek moment”.

DeepSeek's (Open) Weight: In addition to high performance and low cost that created waves in early 2025, DeepSeek’s pioneering as an openly compute-efficient frontier lab is a large part of what has made the company and its models mainstays. These advances can likely be attributed to innovating in a compute-scarce environment. Funded by investor Wenfeng Liang with a " pure pursuit of open source and AGI ," DeepSeek became the most-followed organization on Hugging Face . Its highly detailed technical papers, including a groundbreaking _Nature_-published study on its R1 model, set a new standard for scientific communication. While a large draw is its open-weights over its API, in 2024, DeepSeek slashed its API prices to 1/30th of OpenAI's, triggering a price war. In 2025, DeepSeek-OCR further proved their prowess in compute efficiency and with the release of DeepSeek-V3.2-Exp , they passed on a further 50%+ discount to the users. Notably, DeepSeek’s-V3.2-Exp model was also released with day zero support for deploying on Chinese chips (Huawei’s Ascend and Cambricon). This release also marks emphasis on CUDA alternatives and exemplifies a full-stack hardware-software AI infrastructure in deployment.

The innovation was not confined to model weights alone; it went deep into the software and hardware stack.

Qwen's Ecosystem Dominance: Alibaba is on a path to control a full stack of high performance models and in-house designed chips , reducing reliance on NVIDIA . The company’s Qwen family became a primary resource for global open-source research. Its permissive Apache 2.0 license enabled commercial use, which was a barrier to comparable models that often used more restrictive customs licenses, leading to over 100,000 derivative models on Hugging Face. Alibaba recently unveiled improved chips for better inference, with its PPU being integrated into domestic infrastructure projects .

An Industry-Wide Tidal Wave of Low-Cost, High Efficiency: More open-weight models released boasting SotA performance with significantly lower pricing. Zhipu AI returned with its GLM-4.5 and 4.6 open-weight releases, with both quickly reaching top trending on Hugging Face and 4.6 becoming the top performing open-weight model on LMArena. GLM’s API pricing continually lowered, boasting cost-effectiveness that even offered a $3/month plan as an alternative to Claude Code at 1/5 of the price. While full transparency on the pricing decisions is unclear, efficiency likely plays a strong role.

Compute limitations likely incentivized advancements architecturally, infrastructurally, and in training. Innovations in compute efficiency from open-weight leaders include DeepSeek’s introduction of Multi-head Latent Attention (MLA) and Group Relative Policy Optimization (GRPO). A culture of openness encouraged knowledge sharing and improvements in compute efficiency contributed to lower inference costs, evolving the AI economy.

Domestic silicon’s proven sufficiency has sparked demand and models are beginning to be optimized for domestic chips. In parallel, software platforms are shifting as alternatives to NVIDIA’s CUDA emerge and challenge NVIDIA at every layer; synergy between AI developers and chip vendors are creating a new, fast-evolving software ecosystem.

The shifting global compute landscape will continue to shape open source, training, deployment, and the overall AI ecosystem.