DeepSeek and Zhipu AI have joined OpenAI and Anthropic in developing specialized chips for inference. This move signals a fundamental shift in the industry, as leading artificial intelligence laboratories aim to create their own silicon products to reduce costs and decrease reliance on Graphics Processing Units (GPUs).
According to a Reuters report on July 7, DeepSeek is working on its own AI chip specifically designed for large model inference. The project, which began about a year ago, is gradually expanding its chip design team and collaborating with chip manufacturers, foundries, and memory suppliers.
On the same day, The Information reported that Zhipu AI is considering developing its own AI chips amid growing demand for its GLM series models.
This trend is not limited to China. On June 24, OpenAI unveiled its first proprietary inference chip, codenamed Jalapeño, developed in collaboration with Broadcom. Samples of this chip are already being tested in the lab, with deployment planned for the end of the year. Previously, in April, it was reported that Anthropic was considering creating its own chips, and in June, it hired a key engineer from OpenAI's chip project.
A pattern is emerging: leading companies involved in AI models in both China and the US, regardless of whether they use open or closed strategies, are simultaneously entering the capital-intensive business of chip manufacturing. The economic viability is clear: training advanced models requires significant expenditure, but inference costs accumulate continuously with every user interaction, agent operation, or token generation.
DeepSeek and Zhipu AI are focusing on inference chips to optimize the performance of their MoE architectures, KV cache management, and low-precision computing.
For Chinese AI companies, developing their own chips is also a response to strategic vulnerability. US export controls restrict access to advanced NVIDIA GPUs, making domestic alternatives and proprietary silicon solutions a matter of operational control, not just cost savings.
However, chip development involves enormous risks. Industry estimates suggest that designing an advanced AI chip costs around $500 million, and there are no guarantees of success. The spectrum of self-development includes Google's full-stack TPU approach or partnerships between architecture developers and established firms like Broadcom, as demonstrated by OpenAI's Jalapeño.
There is an accelerating separation of hardware for training and inference. Google split its eighth-generation TPU into 8t for large-scale training and 8i for low-latency inference. Huawei has done something similar, dividing the Ascend 950 series. The moves by DeepSeek and Zhipu AI reflect a broader industry understanding: at the forefront of development, the optimal path requires control over the entire stack—from silicon to the model itself.
DeepSeek, a Chinese startup specializing in artificial intelligence, is investing in the development of its own semiconductor. This new chip's main objective is to execute AI models, thereby reducing the company's dependence on established suppliers such as Nvidia and Huawei.
According to information reported by Reuters, sources close to the matter indicate that DeepSeek is working on a semiconductor dedicated to inference, not the training of new models. This initiative aims to create an internal solution for a stage that has gained great relevance with the proliferation of AI applications.
This move reflects a trend observed in the sector, where various companies are seeking greater control over the equipment necessary to keep their systems operational. Specialized chips, moreover, promise to offer better operational efficiency compared to general-purpose components.
The main targets of this type of semiconductor include reducing dependence on external semiconductor suppliers, meeting the growing processing volume required by AI, increasing control over technological infrastructure, and creating alternatives against restrictions imposed by the United States on advanced chips.
Currently, DeepSeek uses chips supplied by Nvidia and Huawei. Its R1 model, which generated significant buzz in the technology market in January 2025 due to its low operating cost, was trained using Nvidia's H800, a component made for the Chinese market but subsequently banned by the US.
With American restrictions, Huawei has managed to gain ground in the domestic AI chip market. Simultaneously, competitors like Alibaba and Baidu have begun developing their own components to participate in this segment. DeepSeek has also been seeking partnerships with chip design companies, semiconductor manufacturers, and memory companies to boost its project, discreetly increasing the hiring of specialized engineers.
Liang Wenfeng, founder of DeepSeek, had mentioned in an interview given in 2024 that chip export controls represented a significant obstacle for the company.
Developing a competitive chip in the market requires years of dedication, high investment, and access to cutting-edge manufacturing technologies. The latter aspect constitutes one of the biggest challenges for Chinese companies, which face limitations regarding strategic equipment and components.
Nvidia remains unavailable in China. DeepSeek has very few chances of commercializing its chips outside of China unless it can gain access to top-tier manufacturing technologies.
Parallel to the search for technological autonomy, DeepSeek is undergoing a financial restructuring. According to Reuters, the company plans to raise US$7 billion in an initial round, with an estimated valuation between US$52 billion and US$59 billion.
This endeavor demonstrates that competition in the artificial intelligence area is expanding across the entire technological chain, covering everything from consumer-used models to the essential chips for their operation.
A new artificial intelligence model developed in China is attracting significant attention in the technology sector. According to a Reuters report, the GLM-5.2 model from the startup Z.ai demonstrates performance comparable to systems from the United States, but at a significantly lower cost.
Following the emergence of DeepSeek, which showed the competitiveness of cheaper models, other Chinese companies have accelerated their launches. GLM-5.2 is the latest example of this trend. The model, created in Beijing, gained recognition for its ability to perform complex tasks with minimal commands and function as an AI agent, making intermediate decisions in long processes, which reduces the need for constant user intervention.
In Silicon Valley, the system has begun appearing actively on platforms such as OpenRouter and has reached a level of developer usage exceeding that of models from Anthropic. There is now a Chinese open-weight model comparable to current OpenAI and Anthropic models.
David Sachs, a former head of AI policy in the US government during the Donald Trump administration, told Reuters that GLM-5.2 is 'just slightly below Opus 4.8 (from Anthropic) and on par with GPT 5.5 (from OpenAI)'.
The interest in GLM-5.2 is directly linked to the combination of technical power and reduced price. Compared to advanced closed models, operational costs can be about one-sixteenth of the cost.
Key features of this model include: high efficiency in programming and logical reasoning; the ability to act as an AI agent based on simple commands; an open architecture that is easy to implement; and a substantially lower cost compared to direct competitors.
According to Artificial Analysis rankings, GLM-5.2 ranks fifth among large language models. In Code Arena, the model also secured second place in frontend development tasks, such as creating websites and applications.
Despite the technological progress, global adoption has not yet been uniform. The main point of caution remains data security, especially for companies in the US and in regulated sectors.
Nevertheless, part of the market is already testing alternatives deployed on private cloud or internal servers, seeking a balance between risk and cost. Simultaneously, the development of these models expands China's presence in the global AI market and puts pressure on North American competitors, as cost, performance, and open access begin to matter as much as the origin of the technology.
