Apple’s move marks a strategic upgrade in response to the AI industry trend. As large language model technology surges, demand for computing power has skyrocketed, prompting Apple to intervene at the hardware level to mitigate supply chain risks. Notably, the “Baltra” chip is not designed as a universal solution but precisely targets AI inference—a focus aligned with Apple’s current use of Google’s customized Gemini model to power its “Apple Intelligence” services. Inference chips emphasize low latency and high concurrent throughput, optimizing low-precision computing such as INT8 to significantly enhance energy efficiency and response speed, meeting users’ real-time instruction processing needs.
In terms of technical architecture, the “Baltra” design concept overturns traditional training chip models. Apple’s collaboration with Broadcom prioritizes optimizing the chip’s execution capability rather than coping with the massive computing consumption of model training. This stems from Apple’s deep insight into AI application scenarios: from composing emails to Siri requests, inference performance becomes critical. On the supply chain side, the chip is highly likely to use TSMC’s 3nm N3E process, with design work expected to be completed within the next 12 months to ensure on-schedule deployment by 2027—reflecting Apple’s sustained investment in advanced process technology.
From a competitive moat perspective, Apple is building a complete chip empire spanning from endpoint to cloud. Beyond the A/M series, the company is rapidly expanding its proprietary portfolio, including the 5G baseband chip C1, the Wi-Fi/Bluetooth chip N1, and future chips for AI glasses. This not only strengthens ecosystem control but also drives innovation in AI infrastructure. By mastering core technology nodes, Apple aims to create an insurmountable competitive advantage in computing power, guiding the AI industry toward autonomy and efficiency.