AI chip competition intensifies: a computing revolution from training to inference

In 2026, the global AI chip market will enter a stage of intense competition. From high-end GPUs required for training large models to inference chips on terminal devices, the computing revolution is reshaping the entire semiconductor industry landscape. NVIDIA still holds the top spot in the AI chip field, with its Blackwell architecture GPU achieving several fold improvements in training performance. At the same time, AMD is rapidly catching up in the cloud inference market with its MI400 series accelerators, while Intel's Gaudi 3 is also occupying a place in enterprise level deployments. However, the biggest variable comes from the wave of self-developed chips by tech giants. Google's TPU v6 has entered its sixth generation, designed specifically for super large models such as Gemini; Amazon's Trainium 2 is reshaping AWS's AI training ecosystem; Microsoft and Meta have also launched self-developed chips in an attempt to break away from dependence on a single supplier. More noteworthy is the explosion in the field of inference chips. As AI applications move from the cloud to the edge, low-power, high-performance inference chips have become a new battlefield. Groq's LPU architecture has achieved millisecond level inference response, and Chinese manufacturers such as Cambrian and Horizon are also accelerating their catch-up. The ultimate winner of this computing power arms race will determine the direction of AI technology development in the next decade. AI chips are not only hardware, but also the infrastructure of the entire AI industry - whoever controls computing power holds the key to the AI era.