Advancing GPU Programming with the CUDA Tile IR Backend for OpenAI Triton | NVIDIA Technical Blog
… Learn more NVIDIA CUDA Tile is a GPU-based programming model that targets portability for NVIDIA Tensor Cores, unlocking peak GPU performance. …
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How is Ising Calibration 1.5 performance evaluated?
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
NVIDIA Ising Enables Fully Automated Quantum Computer Calibration with Enhanced In-Context Learning | NVIDIA Technical BlogHow 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.
NVIDIA Ising Enables Fully Automated Quantum Computer Calibration with Enhanced In-Context Learning | NVIDIA Technical BlogNVIDIA Ising Enables Fully Automated Quantum Computer Calibration with Enhanced In-Context Learning | NVIDIA Technical Blog
… The model is trained on diverse datasets from multiple qubit modalities and evaluated with the QCalEval benchmark, demonstrating state-of-the-art zero-shot and in-context learning performance, outperforming all open models and remaining competitive with leading closed models in quantum calibration … …
Run DiffusionGemma on NVIDIA for Developer-Ready, High-Throughput Text Generation | NVIDIA Technical Blog
… NIM packages the model as an optimized, containerized inference microservice — with performance tuning, standardized APIs, and the flexibility to run on-premises, in the cloud, or across hybrid environments. …
Streaming Tokens and Tools: Multi-Turn Agentic Harness Support in NVIDIA Dynamo | NVIDIA Technical Blog
… Matching the harness experience depends on a collection of smaller behaviors that are easy to miss in ad-hoc testing: Model metadata at both GET /v1/models and GET /v1/models/{model id} Correct handling of slashed model IDs Useful input tokens in message start Acceptance of cache control Once the f… …
NVIDIA Ising Introduces AI-Powered Workflows to Build Fault-Tolerant Quantum Systems | NVIDIA Technical Blog
… The pre-trained models deliver top performance out of the box, and because everything is open, users can also specialize for their own hardware and noise characteristics while keeping proprietary QPU data on-site. …
Post-Train NVIDIA Cosmos 3 in One Day Using Agent Skills | NVIDIA Technical Blog
… How to optimize a vision model with TAO AutoML LoRA provides a strong first result, but post-training performance often depends on selecting the right configuration. …
How to Run an Autoresearch Workflow with RL Agent Skills and NVIDIA NeMo | NVIDIA Technical Blog
… At NVIDIA, his work spans a wide range of deep learning and AI applications, including large language models and multi-modality models. …
Mitigating Indirect AGENTS.md Injection Attacks in Agentic Environments | NVIDIA Technical Blog
… This trust model is by design, but it creates an interesting attack surface when a malicious dependency is able to write or modify these files at build time. …