ZenteiQ scales physical AI inference 4x with GKE and llm-d
Today we hear fromZenteiQ, an artificial intelligence startup spun out of the Indian Institute of Science (IISc) and selected under theIndiaAI Mission to build sovereign scientific foundation models from the ground up. To pre-train models that understand physical laws, governing equations, and numerical solvers, ZenteiQ designed and built a large-scale batch synthetic data-generation pipeline that runs across multiple model families and feeds verified datasets into training runs.Google Kubernetes Engine (GKE) andGKE Inference Gateway gave ZenteiQ the inference-routing layer within its pipeline to scale aggregate inference throughput 4.07x and grow daily output from 1.47 billion to 5.97 billion generated tokens for every 1,000 accelerators. - Fisayo Feyisetan, Product Manager, Google Cloud
Unlike general-purpose language models trained primarily on web text and code, scientific foundation models must understand complex physical systems, mathematical governing equations, and numerical solvers across domains such as thermodynamics, computational fluid dynamics, and electromagnetics....
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