NVIDIA is AI The layout of the chip field is shifting from "computing power competition" to "efficiency breakthrough", and its future plans mainly revolve around acquisitions Groq Enhance reasoning ability Blackwell Continuous iteration of architecture and CUDA The ecological moat is unfolding, as follows:

1. Acquisition Groq： Seize the high ground of inference chips

Background: In December 2025, Nvidia acquired for $20 billion AI Emerging chip technology Groq， The latter relies on traditional methods GPU The inference speed that is more than 10 times faster is called the "strongest inference chip on the surface".

Strategic significance: This move marks Nvidia's transformation from a "training" to a "reasoning" computing paradigm, aimed at achieving Groq Optimization of reasoning technology AI Cost and speed of application implementation.

2.‌ Blackwell Architecture: Consolidate universality GPU Dominant position

Technological highlights: Blackwell Architecture (B200) passed NVLink High speed interconnection technology builds hardware performance barriers, while relying on CUDA Ecological migration costs.

Market performance: NVIDIA relies on Blackwell architecture and CUDA Ecology, continuously consolidating its dominant position in the field of high-performance general-purpose computing.

3.‌ CUDA Ecology: Building a Soft Hard Integrated moat

Ecological advantages: CUDA Ecology is Nvidia's "trump card", and millions of developers worldwide rely on its underlying logic, resulting in extremely high migration costs.

Domestic substitution challenge: Domestic manufacturers such as Moore Thread and Muxi Technology have passed MUSA Architecture MXMACA Software stack and other technologies attempt to break through CUDA Ecological barriers.

4. Coping with competition: AMD The impact on domestic chips

AMD Challenge: AMD Through open source ROCm ecology and MI300 The cost-effectiveness advantage of the series lies in finding cracks in high-performance computing and customized requirements for specific cloud vendors.

Domestic substitution: Mole Thread, Boren Technology, Muxi Technology and other enterprises have achieved full functionality GPU Architecture Chiplet Heterogeneous integration technology, etc., achieve a leap from "performance benchmarking" to "testing of 10000 card clusters".

5. Future trend: shifting from "violent computing" to "refined operation"

Separation of training and promotion: by 2026, AI The chip industry will completely enter the era of "separation of training and promotion", and chips specifically designed for inference optimization will become the mainstream in the market.

‌ PD Separation: Prefill and Decode The large-scale implementation of cutting-edge architectures such as separation will perform "refinement surgery" on the load characteristics at different stages of the large model generation process, improving the upper limit of computing power throughput and reducing marginal costs.