China’s GPU cloud market is consolidating rapidly around a small number of domestic champions, with Baidu and Huawei emerging as the clear leaders, as access to Nvidia’s most advanced accelerators remains restricted.
A recent Frost & Sullivan report places Baidu and Huawei together at more than 70% of China’s "GPU cloud" market — defined specifically as cloud services built on domestically designed AI chips rather than imported GPUs — reflecting a deliberate shift by Chinese Internet and telecom giants to vertically integrate AI hardware, software frameworks, and cloud services, while a parallel wave of AI chip start-ups races to public markets to fund the next stage of domestic silicon development.
This is all unfolding against the backdrop of U.S. export controls that continue to limit China’s access to leading-edge Nvidia and AMD accelerators. Since late 2022, Chinese firms have been forced to plan AI infrastructure growth around constrained supplies of downgraded GPUs or around entirely domestic alternatives. This has led to a GPU cloud market that looks increasingly different from its Western counterparts, with architectural choices and performance trade-offs shaped as much by geopolitics as the engineering itself.
Vertical integration replaces scaling
(Image credit: Huawei)
Baidu and Huawei’s dominance rests on a shared strategy of full-stack control. Rather than acting as neutral cloud providers that source GPUs from global vendors, both companies design their own AI accelerators, optimize their own software frameworks, and deploy those components at scale inside proprietary data centers.
Baidu’s AI cloud is built around its Kunlun accelerator line, now in its third generation. In April 2025, Baidu disclosed that it had brought online a 30,000-chip training cluster powered entirely by Kunlun processors. According to the company, that cluster is capable of training foundation models with hundreds of billions of parameters and simultaneously supporting large numbers of enterprise fine-tuning workloads. By tightly coupling Kunlun hardware with Baidu’s PaddlePaddle framework and its internal scheduling software, Baidu is compensating for the absence of Nvidia’s CUDA ecosystem with vertical optimization.
Huawei has taken a similar but more industrial-scale approach. Its Ascend accelerator family now underpins a growing share of AI compute deployed by state-owned enterprises and government-backed cloud projects. Huawei’s latest large-scale configuration, based on Ascend 910-series chips, emphasizes dense clustering and high-speed interconnects to offset weaker per-chip performance and lower memory bandwidth compared to Nvidia’s H100-class GPUs. Huawei has been explicit about this trade-off internally, framing cluster-level scaling as the primary path forward while advanced process nodes and EUV lithography remain out of reach.
Both companies are also pursuing heterogeneous cluster designs. Chinese cloud providers have increasingly experimented with mixing different generations and vendors of accelerators within a single training or inference pool. This approach reduces dependence on any single chip supply source but raises software complexity, requiring custom orchestration layers to manage uneven performance characteristics. Baidu and Huawei are among the few firms in China with the engineering resources to make such systems viable at production scale, which further entrenches their market position.
Domestic chips close the gap
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