
OpenAI has unveiled its Jalapeño chip, claiming it surpasses Nvidia's superchips in AI inference tasks. Developed with Broadcom, Jalapeño is an ASIC designed to enhance AI model performance, offering up to 1.9 times more work per watt and significantly lower latency. This advancement promises faster AI responses and more efficient processing. OpenAI plans to deploy the chip in limited quantities by the end of the year, with a gradual increase in production through 2027. Despite its capabilities, OpenAI will continue to collaborate with Nvidia and other partners for its broader compute strategy.
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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
Google launched Gemini 4 Carbon, a new model variant aimed at providing high performance at a lower cost for inference tasks.