Why is NVIDIA the AI leader? Musk, Microsoft, AMD and Amazon followed, and 2025 and 2026 became a watershed in the market

Since Huida (Nvidia) transformed from a gaming GPU supplier to a global AI computing core technology provider, it has successfully gained a foothold in the global AI industry. The world’s major technology giants such as Microsoft (Microsoft), Amazon (Amazon), Google, Meta and Musk (Elon Musk) followed suit and increased its investment in AI infrastructure.

At the same time, billions of dollars are spent to build “hyperscale data centers” and millions of AI chips are purchased to meet the huge demand for computing power of AI models. However, in the face of Huida’s strong dominance, whether opponents such as AMD, Intel (Intel), Google TPUs and Amazon (Tranium) have a chance to break through, whether AI development will encounter computing power bottlenecks in the future, and how this AI war will develop.

“Scale” becomes key, and tech giants continue to build AI infrastructure

In recent years, the demand for AI models has grown rapidly, and technology giants have invested in building large data centers to expand AI computing capabilities, including:

Meta builds 2 gigawatt (2GW) data center in Louisiana, USA.

Amazon AWS and Microsoft Azure are also expanding multiple gigawatt (Gigawatt) data centers around the world.

Google spent billions of dollars on fiber optic networks and strengthened data center links around the world.

Musk’s xAI plans to deploy 200,000 GPUs to build AI supercomputing clusters.

The investment of these technology giants shows that “scale” is still the key, and then connecting global data centers through optical networks to improve AI training and computing performance, directly refutes the claim that “AI computing scale has reached its limit”.

Huida still occupies the lead, relying on it for more than 70% of the world’s AI computing power

In the AI chip market, Huida is still the market leader with a market share of 70%, especially in the field of AI training, with a market share close to 98%. Even though Google has its own TPU (Tensor processing Unit) chips, it is still one of the buyers of Huida GPUs.

( Note: TPUs are special application chips designed by Google to accelerate machine learning and AI workloads. )

Why Huida is able to maintain its dominance

Software advantages: In addition to hardware, Huida is one of the few semiconductor companies that is really good at AI software, especially its own CUDA ecosystem is difficult to replace.

Advanced process: New technologies can always be introduced faster than competitors, ensuring leading chip performance.

Network Technology: Strengthens the (NVLink) of efficient interconnect technology between GPUs with the acquisition of Mellanox.

These factors make NVIDIA a leader in AI chips, and even if AMD, Google, Amazon and other competitors actively invest in AI chip development, Huida still holds a dominant position in the market. However, the market is still highly competitive, and many technology players are looking for alternatives to reduce their dependence on Huida.

Google TPU, AMD MI300, Amazon Tranium can challenge Huida

Although Huida still dominates the market, several major competitors such as Google, AMD and Amazon are still trying to seize the AI computing power market. Although Google’s TPUs are mainly used for internal services such as search, ads, and YouTube video processing, they still have a large market share in the AI training market.

Google TPU advantages and disadvantages

Partnering with Broadcom with powerful interconnect technology.

Integrate into Google’s internal services to optimize search and advertising AI algorithms.

It is second only to NVIDIA in the field of AI training.

TPU disadvantage

Google’s cloud business is weak, and TPU is not as competitive as Huida in the external market.

The software ecosystem is closed and difficult for external developers to use.

AMD MI300 is Huida’s biggest competitor, but the software is still a shortcoming

AMD has actively deployed the AI chip market in recent years, and the MI300X GPU has benefited from the export control of the United States to China, which is relatively popular in the Chinese market. In addition, Microsoft and Meta are also actively using AMD chips to reduce dependence on Huida.

Although AMD is doing well, it will not explode as the market expects. Because AMD’s ROCm platform is not mature enough in terms of AI software ecosystem, there is still room for improvement in stability and ease of use compared to Huida’s CUDA.

For example, when developers use ROCm to train AI models, they often encounter problems such as debugging (debug) difficulties, and even the basic “out of the box” experience is not smooth. Models of mainstream AI frameworks such as running PyTorch directly with ROCm may encounter situations that cannot run smoothly, and additional adjustments or modifications are required, making it less convenient for developers to use.

Amazon Tranium 2 focuses on cost-effectiveness

Amazon’s Tranium 2 is jokingly known as Amazon’s TPU, which is mainly used for internal AI training, emphasizing low cost and high memory bandwidth. Here are Amazon’s three aspects of cost-effectiveness:

Improved price-performance ratio: AWS claims that Trn2 instances offer a 30-40% price performance advantage over current generation GPU-based EC2 instances (P5e, P5en, and using Huida H200).

Increased energy efficiency: Trainium 2 uses up to 3 times more energy than the first-generation Trainium (Trn1) and consumes less energy per unit calculation.

Reduced training costs: Trainium 2 enables efficient distributed training, paired with the Elastic Fabric Adapter (EFA) to provide up to 12.8 Tbps of network bandwidth, reducing the time it takes to train large AI models.

For AWS customers, not only reducing leasing and electricity costs, but also reducing overhead costs through ecosystem integration, especially in the context of the surge in demand for AI chips, this is a key strategy to challenge Huida.

Huida is still ahead, but can the AI computing power capital frenzy continue?

At present, Huida is still holding the supremacy of AI chips, but 2025 and 2026 will be a watershed in the market. If the performance of AI models continues to break through, the demand for computing power will continue to rise, and major technology giants will continue to invest capital to promote industrial development.

However, the market still needs to pay attention to whether the AI business model can really bring stable profits, otherwise the AI market may be “bubbled”, and then it will usher in a wave of consolidation, and only companies with real technological advantages can survive.

(64,000 Huida AI chips are ready to enter! The Stargate project has smashed billions of magnesium, and the Texas super data center is about to launch )

This article Why is NVIDIA firmly at the AI leader? Musk, Microsoft, AMD and Amazon followed, and 2025 and 2026 became a watershed in the market and first appeared in the chain news ABMedia.