🇭🇰SCMP Technology•Freshcollected in 30m
Why China’s AI Still Depends on Nvidia
💡China’s LLM leaders still rely on Nvidia because replacing the full software stack is prohibitively difficult.
⚡ 30-Second TL;DR
What Changed
Nvidia chips remain the standard hardware for training LLMs among major Chinese AI developers.
Why It Matters
The continued reliance on Nvidia gives CUDA-based tooling and established GPU workflows lasting importance in China’s AI ecosystem. Domestic chipmakers may advance technologically but still face a significant software compatibility and migration-cost gap.
What To Do Next
Use PyTorch Profiler to benchmark your CUDA training pipeline and identify the kernels and dependencies that would need porting to a domestic accelerator.
Who should care:Researchers & Academics
Key Points
- •Nvidia chips remain the standard hardware for training LLMs among major Chinese AI developers.
- •Switching to domestic semiconductors requires substantial software and hardware re-engineering.
- •The financial cost of replacing existing Nvidia-based infrastructure is delaying China’s AI hardware transition.
🧠 Deep Insight
AI-generated analysis for this event.
🔑 Enhanced Key Takeaways
- •The CUDA software ecosystem remains the primary 'moat' for Nvidia, as Chinese developers face immense technical debt when attempting to port proprietary CUDA-based code to heterogeneous computing frameworks like Huawei's CANN.
- •U.S. export controls have forced Chinese firms to rely on 'de-tuned' chips like the H20, which offer significantly lower interconnect bandwidth compared to the H100/H200, creating a bottleneck in multi-node cluster training efficiency.
- •Major Chinese cloud providers are increasingly adopting a 'hybrid' strategy, utilizing domestic NPUs for inference tasks while reserving scarce, high-performance Nvidia GPUs exclusively for the pre-training phase of frontier models.
- •The lack of a unified, open-source software stack for domestic Chinese chips has led to fragmented development environments, where each manufacturer (e.g., Huawei, Cambricon, Biren) requires custom optimization for the same LLM architecture.
- •Recent advancements in interconnect technologies, such as Huawei's Ascend 910C, are reportedly narrowing the performance gap, yet supply chain constraints and low manufacturing yields continue to limit large-scale deployment.
📊 Competitor Analysis▸ Show
| Feature | Nvidia (H20/H100) | Huawei (Ascend 910C) | Cambricon (MLU590) |
|---|---|---|---|
| Software Ecosystem | CUDA (Mature/Universal) | CANN (Developing) | Proprietary (Limited) |
| Interconnect | NVLink (Industry Std) | Ascend Fabric (Proprietary) | Proprietary |
| Training Throughput | High (Baseline) | 80-90% of H100 (Claimed) | Moderate |
| Availability | Restricted (Export Control) | High (Domestic) | High (Domestic) |
🛠️ Technical Deep Dive
- Nvidia's H20 GPU utilizes a reduced memory bandwidth of 4.0 TB/s compared to the H100's 3.35 TB/s, but significantly lower compute density to comply with U.S. export regulations.
- Huawei's CANN (Compute Architecture for Neural Networks) acts as the middleware layer attempting to bridge the gap between PyTorch/TensorFlow and Ascend hardware, though it lacks the extensive library support of CUDA.
- Multi-node training in China is currently hampered by the lack of high-speed, low-latency interconnects equivalent to Nvidia's NVLink Switch System, forcing reliance on slower InfiniBand or Ethernet-based clusters.
- Memory wall issues are exacerbated in domestic chips due to the limited availability of HBM3/HBM3e, forcing reliance on slower GDDR6 or HBM2e memory configurations.
🔮 Future ImplicationsAI analysis grounded in cited sources
Chinese AI firms will shift toward 'Model Distillation' as a primary strategy.
The scarcity of high-end training hardware will force developers to train smaller, more efficient models on domestic chips rather than scaling massive parameters.
The 'CUDA-lock' will persist for at least another 24 months.
The sheer volume of legacy code and the lack of a standardized, cross-vendor software abstraction layer in China prevents rapid migration.
⏳ Timeline
2022-10
U.S. Department of Commerce implements initial sweeping export controls on advanced AI chips to China.
2023-10
U.S. updates export rules, effectively banning the sale of high-performance chips like the H800 and A800 to China.
2024-02
Nvidia begins shipping the H20, a compliant, lower-performance GPU specifically designed for the Chinese market.
2025-05
Major Chinese tech giants report significant delays in LLM training timelines due to hardware supply constraints.
2026-03
Huawei announces mass production of the Ascend 910C, aiming to compete directly with Nvidia's H20 in the domestic market.
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Original source: SCMP Technology ↗