
OpenAI has introduced its first in-house AI chip, Jalapeño, which has shown to outperform Nvidia's top GPUs in speed and power efficiency. The chip, developed with Broadcom, is designed for running AI models and not for training them. In tests, Jalapeño operated at 700 watts, delivering up to 3.6 times faster performance and 1.9 times more work per watt compared to Nvidia's 1,200-watt options. OpenAI plans to use the chip internally, with no current plans to sell it, as it continues to rely on Nvidia for training new models. This development marks a significant step in OpenAI's hardware capabilities.
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This release quietly cements llama.cpp as the universal inference runtime by finally supporting NVIDIA's latest CUDA 13 stack alongside AMD's ROCm 10.0. The inclusion of native builds for Linux arm64 with Snapdragon chips, covering CPU, Adreno GPU, and Hexagon NPU, signals a serious push into mobile edge deployment. While Apple Silicon KleidiAI is disabled in this specific build, the broader hardware coverage means developers no longer need to compile from source for most modern accelerators. It’s less about new features and more about removing friction for anyone running local models on bleeding-edge or diverse hardware.
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OpenAI's Jalapeño Chip Boosts AI Inference Efficiency
5 developments
Apple is finally unlocking its AI suite for Chinese users with the iOS 27.3 beta, marking a significant regulatory milestone after months of approval delays. The rollout relies on Alibaba’s Qwen model rather than OpenAI, integrating features like notification summaries and visual intelligence directly into the OS. By sidestepping the new Siri in favor of established text and image tools, Apple is prioritizing immediate utility over the full generative experience. This move effectively brings Apple’s AI capabilities to a massive new market, albeit with localized constraints that distinguish it from the global release.