OpenAI unveiled Jalapeño, its first custom inference processor developed with Broadcom, designed to reduce dependence on Nvidia GPUs. Early testing shows significantly better performance-per-watt than current alternatives, with the chip specifically optimized for low-cost inference on real-time coding models. The move allows OpenAI to control more of its infrastructure stack, potentially improving operating economics and enabling faster, more reliable, and cheaper service delivery.
OpenAI and Broadcom unveiled Jalapeño, a custom AI chip designed for inference workloads, marking OpenAI's first entry into silicon manufacturing. The chip was designed in nine months and will be deployed starting late 2026, with significant scaling expected in 2027 and early 2028. OpenAI aims to reduce dependence on Nvidia GPUs and build a complete technology stack to serve AI models more efficiently and affordably.
IBM announced 0.7 nanometer transistor chips, the smallest in the world, using a new three-dimensional nanostack architecture with innovations in wafer bonding and memory scaling. The chips are 70% more efficient than IBM's previous 2 nanometer chips from 2021, and could theoretically enable AI accelerators to deliver 9,000 TOPS compared to current accelerators' 1,500 TOPS, potentially reducing training time for large language models from three months to two weeks. The nanostack design could support a decade of further chip innovations by stacking transistors vertically rather than only shrinking them horizontally.
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