Rohan Paul on X: "Per The Information, Zhipu AI is also (after DeepSeek) exploring a custom ASIC after GLM-5.2 usage reportedly jumped 27x in one week. A custom ASIC removes flexibility, but it can cut power draw and per-token cost. Nvidia GPUs are strong general-purpose machines, but inference http
Quick Answer
Zhipu AI is exploring a custom ASIC following a 27x increase in GLM-5.2 usage, aiming to reduce power draw and costs.
Quick Take
While Nvidia GPUs excel in general-purpose tasks, dedicated silicon may enhance performance for specific models. The project, which may take over two years, reflects a broader trend among Chinese AI firms to integrate software and hardware more closely.
Key Points
- GLM-5.2 usage surged 27x in one week, prompting Zhipu AI's ASIC exploration.
- Custom ASICs can reduce power draw and per-token costs but lack flexibility.
- Nvidia GPUs are strong general-purpose machines but may not be optimal for all tasks.
- DeepSeek is also developing an inference chip to reduce reliance on Nvidia and Huawei.
- The project may take more than two years and lacks a chosen partner.
📖 Reader Mode
~1 min readPer The Information, Zhipu AI is also (after DeepSeek) exploring a custom ASIC after GLM-5.2 usage reportedly jumped 27x in one week. A custom ASIC removes flexibility, but it can cut power draw and per-token cost. Nvidia GPUs are strong general-purpose machines, but inference at scale has different economics. A fixed model can run better on silicon designed around its own repeated operations. Zhipu has not chosen a partner, and the project may take more than 2 years. The pattern is now bigger than one Chinese lab or one model launch. Chinese AI companies are trying to make software, hardware, and deployment less separable.
— Originally published at x.com
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from WebSearch (Tavily)
See more →全球AI芯片峰会，9月上海见！
The 2026 Global AI Chip Summit will take place in Shanghai on September 22-23, focusing on the evolving AI chip landscape, including the shift from training to inference, the rise of diverse chip technologies, and the restructuring of industry competition. Notable speakers include experts from leading universities and companies, discussing advancements in AI chip architecture and applications.