The AI Chip Race: NVIDIA, AMD and Chinese Chips

The arms race in large AI models is essentially a compute race. Chips are the foundation of the entire AI industry.

NVIDIA: Still the Absolute King
With its CUDA ecosystem and H/B-series GPUs, NVIDIA dominates the training market. Its advantage isn't just hardware — it's 20 years of accumulated software ecosystem. Almost all AI frameworks are optimized around CUDA.

AMD: The Challenger's Breakthrough
MI series is gaining ground in the inference market, competitive in value and VRAM specs. The biggest challenge is ROCm software ecosystem maturity, but progress is evident.

Google TPU: The Self-designed Chip Benchmark
Google's TPU, through self-design + internal use + cloud services, excels in both training and inference efficiency. TPU gives Google a massive internal training cost advantage.

Chinese Chips: Accelerating Catch-up
Huawei Ascend keeps iterating on both training and inference. With improving domestic software stacks (MindSpore, CANN), it's deployed across multiple industries. Cambricon and Hygon have also found niches. The biggest bottleneck remains advanced process manufacturing and interconnect ecosystem.

Key Variables:
1. Advanced process capacity: Determines chip supply ceiling
2. Interconnect technology: NVLink/InfiniBand/Ethernet determines cluster scale efficiency
3. Software ecosystem: The real moat beyond hardware gaps
4. Geopolitical policy: Export controls reshaping global supply

Takeaway for developers and enterprises: Training and inference costs directly affect AI application deployment pace. Focusing on inference optimization (quantization, distillation, sparsification) offers better value than just stacking GPUs.