- ASICs are far more efficient than GPUs for inference, not unlike mining cryptocurrency
- The Inference AI chip market is expected to grow exponentially by the end of this decade
- Hyperscalers like Google have already jumped on the bandwagon
Nvidia, already a leader in AI and GPU technologies, is moving into the Application-Specific Integrated Circuit (ASIC) market to address growing competition and shifting trends in AI semiconductor design.
The global rise of generative AI and large language models (LLMs) has significantly increased the demand for GPUs, and Nvidia CEO Jensen Huang confirmed in 2024 the company will recruit 1000 engineers in Taiwan.
Now, as reported by Taiwan’s Commercial Times (originally published in Chinese), the company has now established a new ASIC department and is actively recruiting talent.
The rise of inference chips
Nvidia’s H series GPUs optimized for AI learning tasks have been widely adopted for training AI models. However, the AI semiconductor market is undergoing a shift toward inference chips, or ASICs.
This surge is driven by the demand for chips optimized for real-world AI applications, such as large language models and generative AI. Unlike GPUs, ASICs offer superior efficiency for inference tasks, as well as cryptocurrency mining.
According to Verified Market Research, the inference AI chip market is projected to rise from a 2023 valuation of $15.8 billion to $90.6 billion by 2030.
Major tech players including Google have already embraced custom ASIC designs in its AI chip “Trillium”, made generally available in December 2024.
The shift toward custom AI chips has intensified competition among semiconductor giants. Companies such as Broadcom and Marvell have surged in relevance and stock value as they collaborate with cloud service providers to develop specialized chips for data centers.
To stay ahead, Nvidia’s new ASIC department focuses on leveraging local expertise by recruiting from leading companies like MediaTek.