NVIDIA and Microsoft Advance Development on RTX AI PCs

Generative AI is transforming PC software into breakthrough experiences - from digital humans to writing assistants, intelligent agents and creative tools.

NVIDIA RTX AI PCs are powering this transformation with technology that makes it simpler to get started experimenting with generative AI and unlock greater performance on Windows 11.

NVIDIA TensorRT has been reimagined for RTX AI PCs, combining industry-leading TensorRT performance with just-in-time, on-device engine building and an 8x smaller package size for seamless AI deployment to more than 100 million RTX AI PCs.

Announced at Microsoft Build, TensorRT for RTX is natively supported by Windows ML - a new inference stack that provides app developers with both broad hardware compatibility and state-of-the-art performance.

For developers looking for AI features ready to integrate, NVIDIA software development kits (SDKs) offer a wide array of options, from NVIDIA DLSS to multimedia enhancements like NVIDIA RTX Video. This month, top software applications from Autodesk, Bilibili, Chaos, LM Studio and Topaz Labs are releasing updates to unlock RTX AI features and acceleration.

AI enthusiasts and developers can easily get started with AI using NVIDIA NIM - prepackaged, optimized AI models that can run in popular apps like AnythingLLM, Microsoft VS Code and ComfyUI. Releasing this week, the FLUX.1-schnell image generation model will be available as a NIM microservice, and the popular FLUX.1-dev NIM microservice has been updated to support more RTX GPUs.

Those looking for a simple, no-code way to dive into AI development can tap into Project G-Assist - the RTX PC AI assistant in the NVIDIA app - to build plug-ins to control PC apps and peripherals using natural language AI. New community plug-ins are now available, including Google Gemini web search, Spotify, Twitch, IFTTT and SignalRGB.

Accelerated AI Inference With TensorRT for RTX Today's AI PC software stack requires developers to compromise on performance or invest in custom optimizations for specific hardware.

Windows ML was built to solve these challenges. Windows ML is powered by ONNX Runtime and seamlessly connects to an optimized AI execution layer provided and maintained by each hardware manufacturer.

For GeForce RTX GPUs, Windows ML automatically uses the TensorRT for RTX inference library for high performance and rapid deployment. Compared with DirectML, TensorRT delivers over 50% faster performance for AI workloads on PCs.

TensorRT delivers over 50% faster performance for AI workloads on PCs than DirectML. Performance measured on GeForce RTX 5090. Windows ML also delivers quality-of-life benefits for developers. It can automatically select the right hardware - GPU, CPU or NPU - to run each AI feature, and download the execution provider for that hardware, removing the need to package those files into the app. This allows for the latest TensorRT performance optimizations to be delivered to users as soon as they're ready.

TensorRT performance optimizations are delivered to users as soon as they're ready. TensorRT, a library originally built for data centers, has been redesigned for RTX AI PCs. Instead of pre-generating TensorRT engines and packaging them with the app, TensorRT for RTX uses just-in-time, on-device engine building to optimize how the AI model is run for the user's specific RTX GPU in mere seconds. And the library's packaging has been streamlined, reducing its file size significantly by 8x.

TensorRT for RTX is available to developers through the Windows ML preview today, and will be available as a standalone SDK at NVIDIA Developer in June.

Developers can learn more in the TensorRT for RTX launch blog or Microsoft's Windows ML blog.

Expanding the AI Ecosystem on Windows 11 PCs Developers looking to add AI features or boost app performance can tap into a broad range of NVIDIA SDKs. These include NVIDIA CUDA and TensorRT for GPU acceleration; NVIDIA DLSS and Optix for 3D graphics; NVIDIA RTX Video and Maxine for multimedia; and NVIDIA Riva and ACE for generative AI.

Top applications are releasing updates this month to enable unique features using these NVIDIA SDKs, including:

LM Studio, which released an update to its app to upgrade to the latest CUDA version, increasing performance by over 30%.

Topaz Labs, which is releasing a generative AI video model to enhance video quality, accelerated by CUDA.

Chaos Enscape and Autodesk VRED, which are adding DLSS 4 for faster performance and better image quality.

Bilibili, which is integrating NVIDIA Broadcast features such as Virtual Background to enhance the quality of livestreams.

NVIDIA looks forward to continuing to work with Microsoft and top AI app developers to help them accelerate their AI features on RTX-powered machines through the Windows ML and TensorRT integration.

Local AI Made Easy With NIM Microservices and AI Blueprints Getting started with developing AI on PCs can be daunting. AI developers and enthusiasts have to select from over 1.2 million AI models on Hugging Face, quantize it into a format that runs well on PC, find and install all the dependencies to run it, and more.

NVIDIA NIM makes it easy to get started by providing a curated list of AI models, prepackaged with all the files needed to run them and optimized to achieve full performance on RTX GPUs. And since they're containerized, the same NIM microservice can be run seamlessly across PCs or the cloud.

NVIDIA NIM microservices are available to download through build.nvidia.com or through top AI apps like Anything LLM, ComfyUI and AI Toolkit for Visual Studio Code.

During COMPUTEX, NVIDIA will release the FLUX.1-schnell NIM microservice - an image generation model from Black Forest Labs for fast image generation - and update the FLUX.1-dev NIM microservice to add compatibility for a wide range of GeF

LINK:	https://blogs.nvidia.com/blog/rtx-ai-garage-computex-microsoft-build/...
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