Graiphic’s LabVIEW Annotation Tool brings dataset import, annotation, augmentation, local model training, testing and LabVIEW project generation into one coherent SOTA workflow. No mandatory cloud dependency, no fragmented toolchain and no Python scripting required for the standard user workflow.
A GPU alone does not guarantee a faster LabVIEW application. This technical guide explains how ONNX Runtime Execution Providers optimize and partition computation graphs, how GraphMX connects them to LabVIEW, and what Graiphic’s open GPU benchmarks reveal about full-graph execution, memory transfers, and local high-throughput processing.
SOTA is Graiphic’s unified AI ecosystem for LabVIEW, covering deep learning, computer vision, generative AI and GPU acceleration. This guide explains how SOTA works and how to install the platform, its toolkits, drivers, add-ons and licenses.
Graiphic has integrated OpenCV as a core technology of the LabVIEW Computer Vision Toolkit. Available through SOTA, it brings acquisition, image processing, ROI management, inspection, display, video workflows and AI-ready vision pipelines directly into LabVIEW.
Kimi K3 shows how quickly open-weight AI is advancing. With GGUF, llama.cpp and the LabVIEW GenAI Toolkit, SOTA gives the LabVIEW community a practical path toward local, multimodal and hardware-aware industrial AI.
SOTA now integrates the llama.cpp runtime, enabling LabVIEW developers to load and run compatible GGUF models directly in LabVIEW without converting them to ONNX or PyTorch. This brings local and quantized LLM inference to industrial, research, and test and measurement applications.
Hugging Face is now available in SOTA. With this new add-on, LabVIEW developers can load and run SafeTensors models natively using the LabVIEW Deep Learning Toolkit, directly from the SOTA platform.
FROG is moving from vision to executable proof. The project now exposes a bounded path from open graphical source to FIR, lowering, backend contracts, runtime acceptance, and LLVM proof — a first step toward accountable graphical programming in the AI era.
What if running an AI model in LabVIEW felt as natural as using a driver? DeepMX is Graiphic’s new execution layer for AI models in LabVIEW, designed to make hardware-optimized inference simple, accessible, and industrial-ready. And this is only the beginning of a much larger stack.
FROG is Graiphic’s new open graphical dataflow language initiative: a hardware-agnostic, source-transparent foundation for building serious software beyond the syntax-first bottleneck.
Graiphic has joined the LabVIEW Usergroup Central Europe to share its SOTA sovereign development ecosystem for AI driven test systems. Expect deep learning, computer vision, accelerators, and GenAI toolkits, plus new releases and a clear roadmap.
LabVIEW doesn’t just let you write code, it lets you see it. As LabVIEW turns 40 and NI turns 50, this article revisits the power of graphical dataflow and explores why the AI shift makes visual, system level thinking more relevant than ever.
2025 was a paradoxical year for Graiphic: financially difficult, yet extraordinarily innovative. From the launch of SOTA to the emergence of Graph Orchestration, deep learning, GenAI, and hardware ambitions, this article reflects on what was built and reveals Graiphic’s vision for making LabVIEW a true language for AI in 2026 and beyond.
We’ve published the Graiphic GO Whitepaper Series on GitHub: four documents that describe a unified, ONNX-native, LabVIEW-based approach to graph computing—bringing AI, logic, and hardware orchestration under one visual environment. SOTA is the foundation, already functional and ready for industry, research, and academia.
We open-sourced our full LabVIEW benchmark suite and results. See why graph-compiled execution (TensorRT/CUDA via ONNX Runtime) consistently outpaces DLL-style approaches—and how DirectML broadens GPU support to AMD/Intel on Windows.
NEST redefines building intelligence through self-learning AI that runs locally, privately, and efficiently — no cloud, no compromise.
Graiphic has been featured by the AI, Data and Robotics Association (ADRA) for its contribution to Europe’s sovereign and energy-efficient AI through GO Hardware (GO HW) and SOTA — two ONNX-based technologies bridging AI, logic, and hardware.
What if he was right? If Graiphic’s engineers see clearly, a modernized LabVIEW could become the universal, frugal, efficient cockpit for AI and robotics—awakening NI’s true potential and sparking a Texas-born revolution.
As AI workloads surge, energy has become a first-class challenge. While Big Tech bets on nuclear-powered cloud datacenters, most industrial, medical, and embedded applications thrive on efficient local execution. At Graiphic, we focus on sovereignty through sobriety: SOTA and LabVIEW everywhere enable energy-aware AI that runs directly on existing hardware, reducing cost, latency, and power consumption.
Learn how Informed Machine Learning (IML) combines domain expertise with AI to enhance accuracy and sustainability. With SOTA and CIAD’s groundbreaking advancements, Graiphic leads the way in creating eco-friendly and efficient AI solutions.
This guide demonstrates how to create executables (2nd method – Builder tool) from HAIBAL Deep Learning, TIGR Vision, and PERRINE Tensor Processing toolkits in LabVIEW.
Explore the latest HAIBAL update, enhancing your Deep Learning projects in LabVIEW with new features
This guide demonstrates how to create executables from HAIBAL Deep Learning, TIGR Vision, and PERRINE Tensor Processing toolkits in LabVIEW. It simplifies deploying AI, computer vision, and tensor computation projects by converting them into standalone applications. Ideal for developers aiming to distribute LabVIEW-based solutions efficiently.
LabVIEW NXG reborn, LabVIEW Copilot LLM of 55 Bilions parameters, LabVIEW Jetson Nano, discover the ambitious Graiphic roadmap for 2024
We're excited to announce the upcoming release of the LabVIEW Perrine accelerator toolkit. To make HAIBAL deep...
IntroductionIn the dynamic world of deep learning, Keras, TensorFlow, PyTorch, and HAIBAL stand as prominent...
🚀 Our company, Graiphic, has always believed that 𝐋𝐚𝐛𝐕𝐈𝐄𝐖 is the ultimate technological language of the future....
All release notes are available at this page .Download link Release NotesV1.3.6 Date of release July 2023 Features...
All release notes are available at this page .Download link Release NotesV1.3.5 Date of release 28 april 2023 Features...
All release notes are available at this page .Download link Release NotesV1.2.1 Date of release 02 february 2023...
All release notes are available at this page .Download link Release NotesV1.2.0 Date of release 22 january 2022...
All release notes are available at this page .Download link Release NotesV1.1.0 Date of release 11 january 2022...
All release notes are available at this page . Release NotesV1.0.0 Date of release 12 december 2022 FeaturesLayers...
A first version of the Cuda integration is now used on HAIBAL to allow you to get the best out of your NVIDIA graphics...
HAIBAL deep learning library for LabVIEW is still under devellopment. Our team worked last week on the integration of...
As we have recently decided to better communicate on the HAIBAL project by making a weekly status, I will start this...
The LabVIEW HAIBAL software library includes a complete basic development kit to seamlessly create accelerated deep...
BASIC NUMBER RECOGNITION EXEMPLE This example, implemented natively in the HAIBAL library, aims to understand how to...
RELEASE DATE As we have finished the functional part of the library and are starting to work on the optimization part...
A LITTLE HISTORY In 2016 Redmon, Divvala, Girschick and Farhadi revolutionized object detection with a paper...
By persevering, we can achieve anything. It’s hot but we are getting there. 8 months ago, PyTorch (Meta)...
A LITTLE HISTORY VGG is a convolutional neural network proposed by K. Simonyan and A. Zisserman from Oxford University...
All layer are now coded in native LabVIEW. First test of importe HDF5 from Keras Tensorflow and a graph generator...
After testing our first full connected neural network, we are now able to do our first 2D convolution in LabVIEW. Now...
Every journey has a beginning and let's bet that we will succeed in developing a complete deep learning library that...
