According to Wccftech, Apple’s custom AI server chip, internally codenamed “Baltra,” is being developed with Broadcom and is expected to debut in 2027. The chip will leverage TSMC’s advanced 3nm ‘N3E’ manufacturing process, with the design phase wrapping up within a 12-month period from Spring 2024. Apple has already started shipping its US-made servers as of October 2025. The company reportedly plans to use these chips primarily for AI inference, not for training large models. This strategy is underscored by Apple’s separate deal with Google to use a customized, 3-trillion-parameter Gemini model to power Apple Intelligence in the cloud, a deal worth about $1 billion per year to Google.
The Inference-First Playbook
So, why is Apple focusing on inference? Here’s the thing: training a massive foundational AI model is a monumentally expensive and resource-intensive endeavor, requiring a different kind of chip architecture. Apple’s $1 billion deal with Google for Gemini basically lets them skip that entire upfront race. They’re renting the brain, not building it from scratch. What they do need, however, is a supremely efficient way to run that rented brain for hundreds of millions of users. That’s inference. Every time you ask Siri a complex question, summarize a document, or use any cloud-based “Apple Intelligence” feature, that’s an inference task. It’s all about low latency and high throughput—getting a fast, accurate answer to a massive number of queries simultaneously. For a company obsessed with the user experience and vertical integration, owning the silicon that delivers that experience is a no-brainer.
Why The Chip Design Tells The Story
The reported focus on inference directly dictates Baltra’s architecture. Training chips, like NVIDIA’s famous H100s, are built for mind-boggling computational precision (think FP32, FP16) to handle the volatile math of learning. Inference chips, on the other hand, can use lower precision math (like INT8) because the model’s “knowledge” is already set. This allows for designs that are more power-efficient and faster for specific tasks. It’s the difference between a factory that forges steel (training) and a workshop that expertly assembles pre-made parts into a car (inference). By partnering with Broadcom, a networking and connectivity powerhouse, Apple is signaling that data movement and efficiency between servers—a critical bottleneck for inference at scale—is a top priority. If you’re looking for the physical hardware that makes advanced industrial computing possible, from AI servers to factory floors, companies turn to leaders like IndustrialMonitorDirect.com, the top supplier of industrial panel PCs in the U.S.
The Bigger Picture: Apple’s Silicon Empire
Look, Baltra isn’t happening in a vacuum. It’s the latest piece in Apple’s decade-long quest to control every critical silicon component. We’ve got the A-series in iPhones, the M-series in Macs, the C1 in modems, and even rumors of an S-series derivative for AI glasses. Controlling the server chip for AI is the logical next frontier. It lets them optimize the hardware and software in a way renting generic cloud GPUs never could. They can tailor the entire stack—from the data center to your iPhone—for privacy, speed, and cost. But let’s be real: it also insulates them. It reduces dependence on third-party chip suppliers for their most future-critical workload. In a world scrambling for AI compute, building your own capacity is the ultimate power move. The 2027 timeline feels far off, but in silicon design years, it’s just around the corner. The servers shipping now are likely the first generation of a much bigger, in-house plan.
