NVIDIA Ising Calibration is an open source vision language model (VLM) designed to interpret diagnostic outputs from quantum processors and determine how they should be tuned to continue operating.
This post introduces the latest model release, NVIDIA Ising Calibration 1.5, which advances AI-based QPU calibration by analyzing unfamiliar diagnostic results without prior training examples. Ising Calibration 1.5 also uses examples from related experiments when available and is 11.4% smaller at BF16 precision. This eases the deployment of agentic calibration workflows directly in local lab environments.
For the first time, the model is also available in an NVFP4-quantized version, enabling deployment on a single GPU or an NVIDIA DGX Spark—comparable with leading closed models such as Fable 5 and GPT 5.6 Sol.
How is the Ising Calibration 1.5 model trained?
The Ising Calibration 1.5 model is trained on data generated from partner contributions across multiple qubit modalities, including superconducting qubits, quantum dots, ions, neutral atoms, electrons on Helium, and others specializing in calibration and control.
