Qualcomm (QCOM) has introduced dedicated AI chips

 AI Overview

Yes, Qualcomm (QCOM) has introduced dedicated 

AI chips designed for data centers, the AI200 and AI250, which are application-specific integrated circuits (ASICs) similar in concept to Google's Tensor Processing Units (TPUs) as specialized AI accelerators. 

Qualcomm's AI Chip Offerings

Qualcomm's chips are specifically designed as high-efficiency, inference-optimized solutions for running generative AI models in a data center environment, rather than the power-intensive training of models that the high-end Nvidia GPUs and Google's TPUs are often used for. They aim to challenge Nvidia's market dominance by offering superior power efficiency and a lower total cost of ownership (TCO) for customers. 

• AI200 and AI250: Unveiled in October 2025, these chips feature a rack-scale solution with high memory capacity (up to 768GB of LPDDR per card) to handle large language models. The AI250 model also incorporates a "near-memory computing" architecture for improved memory bandwidth.
• Target Market: Unlike Google's TPUs, which were initially developed for Google's internal use within its cloud platform, Qualcomm's chips are commercial products intended for sale to various enterprises, hyperscalers, and AI companies like Humain.
• Differentiation: While Nvidia's GPUs rely heavily on the CUDA software platform which is an industry standard, Qualcomm's value proposition is its hardware's power efficiency and cost-effectiveness, though winning market share will require time to build a comparable software ecosystem. 

In summary, while Google's TPUs and Qualcomm's new AI chips (AI200/AI250) are both custom ASICs for AI workloads, Qualcomm is positioning its products as highly efficient, cost-effective alternatives focused on the inference side of the AI market, directly competing with Nvidia's offerings.