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Broadest Ecosystem Every layer of the AI stack is accelerated on NVIDIA - CUDA libraries, open-source frameworks, SDKs, and models - with the broadest community and ISV support. Easy-to-Adopt Tools NVIDIA and the open-source community partner closely to optimize popular AI tools like ComfyUI, Hermes, llama.cpp, Ollama, ONNX, OpenClaw, PyTorch, vLLM and more. These tools can be installed easily and run on the same CUDA stack across NVIDIA local and cloud GPUs. Day 0 Support for Latest Models From LLMs to image generators, new open source models run fast on NVIDIA GPUs from day 0 due to close c

Choose hardware based on operating system, available GPU or unified memory, model size, and workflow. Prototype on NVIDIA DGX Spark™, run AI and agent workflows on GeForce RTX™, NVIDIA RTX Spark™ or NVIDIA RTX PRO™, and scale to NVIDIA DGX Station™ for larger local models and long-running agents. Best For System Role Memory OS Form Factor Model Capacity NVIDIA RTX Spark Prototype & Test Large AI Models and Agents Single Lightweight System Up to 128GB Unified Windows Laptop / Compact Desktop Up to 200 B NVIDIA DGX Spark Prototype & Test Large AI Models and Agents Companion System Up to 128GB

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. 

Performance of Ising Calibration 1.5 is evaluated using the QCalEval benchmark, which measures a model’s ability to interpret experimental results, classify outcomes, evaluate significance, assess fit quality and key features, and recommend next steps. For additional details on the benchmark, model architecture, and evaluation results, see QCalEval: Benchmarking Vision-Language Models for Quantum Calibration Plot Understanding. The evaluation covers both zero-shot and in-context learning (ICL). Zero-shot reasoning analyzes results independently, while ICL evaluates results in the context of re