NVIDIA Unveils Local AI Foundation Models for RTX AI PCs at CES 2025

Unveiling a New Era of Local AI With NVIDIA NIM Microservices and AI Blueprints

New NIM microservices and AI Blueprints unlock generative AI on RTX AI PCs and workstation -- plus, more announcements from CES recapped in this first installment of the RTX AI Garage series. Over the past year, generative AI has transformed the way people live, work and play, enhancing everything from writing and content creation to gaming, learning and productivity. PC enthusiasts and developers are leading the charge in pushing the boundaries of this groundbreaking technology. Countless times, industry-defining technological breakthroughs have been invented in one place -- a garage. This week marks the start of the RTX AI Garage series, which will offer routine content for developers and enthusiasts looking to learn more about NVIDIA NIM microservices and AI Blueprints, and how to build AI agents, creative workflow, digital human, productivity apps and more on AI PCs. Welcome to the RTX AI Garage. This first installment spotlights announcements made earlier this week at CES, including new AI foundation models available on NVIDIA RTX AI PCs that take digital humans, content creation, productivity and development to the next level. These models -- offered as NVIDIA NIM microservices -- are powered by new GeForce RTX 50 Series GPUs. Built on the NVIDIA Blackwell architecture, RTX 50 Series GPUs deliver up to 3,352 trillion AI operations per second of performance, 32GB of VRAM and feature FP4 compute, doubling AI inference performance and enabling generative AI to run locally with a smaller memory footprint. NVIDIA also introduced NVIDIA AI Blueprints -- ready-to-use, preconfigured workflows, built on NIM microservices, for applications like digital humans and content creation. NIM microservices and AI Blueprints empower enthusiasts and developers to build, iterate and deliver AI-powered experiences to the PC faster than ever. The result is a new wave of compelling, practical capabilities for PC users. Fast-Track AI With NVIDIA NIM There are two key challenges to bringing AI advancements to PCs. First, the pace of AI research is breakneck, with new models appearing daily on platforms like Hugging Face, which now hosts over a million models. As a result, breakthroughs quickly become outdated. Second, adapting these models for PC use is a complex, resource-intensive process. Optimizing them for PC hardware, integrating them with AI software and connecting them to applications requires significant engineering effort. NVIDIA NIM helps address these challenges by offering prepackaged, state-of-the-art AI models optimized for PCs. These NIM microservices span model domains, can be installed with a single click, feature application programming interfaces (APIs) for easy integration, and harness NVIDIA AI software and RTX GPUs for accelerated performance. At CES, NVIDIA announced a pipeline of NIM microservices for RTX AI PCs, supporting use cases spanning large language models (LLMs), vision-language models, image generation, speech, retrieval-augmented generation (RAG), PDF extraction and computer vision. The new Llama Nemotron family of open models provide high accuracy on a wide range of agentic tasks. The Llama Nemotron Nano model, which will be offered as a NIM microservice for RTX AI PCs and workstations, excels at agentic AI tasks like instruction following, function calling, chat, coding and math. Soon, developers will be able to quickly download and run these microservices on Windows 11 PCs using Windows Subsystem for Linux (WSL). To demonstrate how enthusiasts and developers can use NIM to build AI agents and assistants, NVIDIA previewed Project R2X, a vision-enabled PC avatar that can put information at a user's fingertips, assist with desktop apps and video conference calls, read and summarize documents, and more. Sign up for Project R2X updates. By using NIM microservices, AI enthusiasts can skip the complexities of model curation, optimization and backend integration and focus on creating and innovating with cutting-edge AI models. What's in an API? An API is the way in which an application communicates with a software library. An API defines a set of "calls" that the application can make to the library and what the application can expect in return. Traditional AI APIs require a lot of setup and configuration, making AI capabilities harder to use and hampering innovation. NIM microservices expose easy-to-use, intuitive APIs that an application can simply send requests to and get a response. In addition, they're designed around the input and output media for different model types. For example, LLMs take text as input and produce text as output, image generators convert text to image, speech recognizers turn speech to text and so on. The microservices are designed to integrate seamlessly with leading AI development and agent frameworks such as AI Toolkit for VSCode, AnythingLLM, ComfyUI, Flowise AI, LangChain, Langflow and LM Studio. Developers can easily download and deploy them from build.nvidia.com. By bringing these APIs to RTX, NVIDIA NIM will accelerate AI innovation on PCs. Enthusiasts are expected to be able to experience a range of NIM microservices using an upcoming release of the NVIDIA ChatRTX tech demo. A Blueprint for Innovation By using state-of-the-art models, prepackaged and optimized for PCs, developers and enthusiasts can quickly create AI-powered projects. Taking things a step further, they can combine multiple AI models and other functionality to build complex applications like digital humans, podcast generators and application assistants. NVIDIA AI Blueprints, built on NIM microservices, are reference implementations for complex AI workflows. They help developers connect several components, including libraries, software development kits and AI models, together in a single application. AI Blueprints include everything that a developer needs to build, run, customize and extend the reference workflow, which includes the reference application and source code, sample data, and documentation for customization and orchestration of the different components. At CES, NVIDIA announced two AI Blueprints for RTX: one for PDF to podcast, which lets users generate a podcast from any PDF, and another for 3D-guided generative AI, which is based on FLUX.1 [dev] and expected be offered as a NIM microservice, offers artists greater control over text-based image generation. With AI Blueprints, developers can quickly go from AI experimentation to AI development for cutting-edge workflows on RTX PCs and workstations. Built for Generative AI The new GeForce RTX 50 Series GPUs are purpose-built to tackle complex generative AI challenges, featuring fifth-generation Tensor Cores with FP4 support, faster G7 memory and an AI-management processor for efficient multitasking between AI and creative workflows. The GeForce RTX 50 Series adds FP4 support to help bring better performance and more models to PCs. FP4 is a lower quantization method, similar to file compression, that decreases model sizes. Compared with FP16 -- the default method that most models feature -- FP4 uses less than half of the memory, and 50 Series GPUs provide over 2x performance compared with the previous generation. This can be done with virtually no loss in quality with advanced quantization methods offered by NVIDIA TensorRT Model Optimizer. For example, Black Forest Labs' FLUX.1 [dev] model at FP16 requires over 23GB of VRAM, meaning it can only be supported by the GeForce RTX 4090 and professional GPUs. With FP4, FLUX.1 [dev] requires less than 10GB, so it can run locally on more GeForce RTX GPUs. With a GeForce RTX 4090 with FP16, the FLUX.1 [dev] model can generate images in 15 seconds with 30 steps. With a GeForce RTX 5090 with FP4, images can be generated in just over five seconds. Get Started With the New AI APIs for PCs NVIDIA NIM microservices and AI Blueprints are expected to be available starting next month, with initial hardware support for GeForce RTX 50 Series, GeForce RTX 4090 and 4080, and NVIDIA RTX 6000 and 5000 professional GPUs. Additional GPUs will be supported in the future. NIM-ready RTX AI PCs are expected to be available from Acer, ASUS, Dell, GIGABYTE, HP, Lenovo, MSI, Razer and Samsung, and from local system builders Corsair, Falcon Northwest, LDLC, Maingear, Mifcon, Origin PC, PCS and Scan. GeForce RTX 50 Series GPUs and laptops deliver game-changing performance, power transformative AI experiences, and enable creators to complete workflows in record time. Rewatch NVIDIA CEO Jensen Huang's keynote to learn more about NVIDIA's AI news unveiled at CES.

Nvidia unveils AI foundation models running on RTX AI PCs

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Nvidia today announced foundation models running locally on Nvidia RTX AI PCs that supercharge digital humans, content creation, productivity and development. GeForce has long been a vital platform for AI developers. The first GPU-accelerated deep learning network, AlexNet, was trained on the GeForce GTXTM 580 in 2012 -- and last year, over 30% of published AI research papers cited the use of GeForce RTX. Jensen Huang, CEO of Nvidia, made the announcement during his CES 2025 opening keynote. Now, with generative AI and RTX AI PCs, anyone can be a developer. A new wave of low-code and no-code tools, such as AnythingLLM, ComfyUI, Langflow and LM Studio enable enthusiasts to use AI models in complex workflows via simple graphical user interfaces. NIM microservices connected to these GUIs will make it effortless to access and deploy the latest generative AI models. Nvidia AI Blueprints, built on NIM microservices, provide easy-to-use, preconfigured reference workflows for digital humans, content creation and more. To meet the growing demand from AI developers and enthusiasts, every top PC manufacturer and system builder is launching NIM-ready RTX AI PCs. "AI is advancing at light speed, from perception AI to generative AI and now agentic AI," said Huang. "NIM microservices and AI Blueprints give PC developers and enthusiasts the building blocks to explore the magic of AI." The NIM microservices will also be available with Nvidia Digits, a personal AI supercomputer that provides AI researchers, data scientists and students worldwide with access to the power of Nvidia Grace Blackwell. Project Digits features the new Nvidia GB10 Grace Blackwell Superchip, offering a petaflop of AI computing performance for prototyping, fine-tuning and running large AI models. Making AI NIMble Foundation models -- neural networks trained on immense amounts of raw data -- are the building blocks for generative AI. Nvidia will release a pipeline of NIM microservices for RTX AI PCs from top model developers such as Black Forest Labs, Meta, Mistral and Stability AI. Use cases span large language models (LLMs), vision language models, image generation, speech, embedding models for retrieval-augmented generation (RAG), PDF extraction and computer vision. "Making FLUX an Nvidia NIM microservice increases the rate at which AI can be deployed and experienced by more users, while delivering incredible performance," said Robin Rombach, CEO of Black Forest Labs, oin a statement. Nvidia today also announced the Llama Nemotron family of open models that provide high accuracy on a wide range of agentic tasks. The Llama Nemotron Nano model will be offered as a NIM microservice for RTX AI PCs and workstations, and excels at agentic AI tasks like instruction following, function calling, chat, coding and math. NIM microservices include the key components for running AI on PCs and are optimized for deployment across NVIDIA GPUs -- whether in RTX PCs and workstations or in the cloud. Developers and enthusiasts will be able to quickly download, set up and run these NIM microservices on Windows 11 PCs with Windows Subsystem for Linux (WSL). "AI is driving Windows 11 PC innovation at a rapid rate, and Windows Subsystem for Linux (WSL) offers a great cross-platform environment for AI development on Windows 11 alongside Windows Copilot Runtime," said Pavan Davuluri, corporate vice president of Windows at Microsoft, in a statement. "Nvidia NIM microservices, optimized for Windows PCs, give developers and enthusiasts ready-to-integrate AI models for their Windows apps, further accelerating deployment of AI capabilities to Windows users." The NIM microservices, running on RTX AI PCs, will be compatible with top AI development and agent frameworks, including AI Toolkit for VSCode, AnythingLLM, ComfyUI, CrewAI, Flowise AI, LangChain, Langflow and LM Studio. Developers can connect applications and workflows built on these frameworks to AI models running NIM microservices through industry-standard endpoints, enabling them to use the latest technology with a unified interface across the cloud, data centers, workstations and PCs. Enthusiasts will also be able to experience a range of NIM microservices using an upcoming release of the Nvidia ChatRTX tech demo. Putting a Face on Agentic AI To demonstrate how enthusiasts and developers can use NIM to build AI agents and assistants, Nvidia today previewed Project R2X, a vision-enabled PC avatar that can put information at a user's fingertips, assist with desktop apps and video conference calls, read and summarize documents, and more. The avatar is rendered using Nvidia RTX Neural Faces, a new generative AI algorithm that augments traditional rasterization with entirely generated pixels. The face is then animated by a new diffusion-based NVIDIA Audio2FaceTM-3D model that improves lip and tongue movement. R2X can be connected to cloud AI services such as OpenAI's GPT4o and xAI's Grok, and NIM microservices and AI Blueprints, such as PDF retrievers or alternative LLMs, via developer frameworks such as CrewAI, Flowise AI and Langflow. AI Blueprints Coming to PC NIM microservices are also available to PC users through AI Blueprints -- reference AI workflows that can run locally on RTX PCs. With these blueprints, developers can create podcasts from PDF documents, generate stunning images guided by 3D scenes and more. The blueprint for PDF to podcast extracts text, images and tables from a PDF to create a podcast script that can be edited by users. It can also generate a full audio recording from the script using voices available in the blueprint or based on a user's voice sample. In addition, users can have a real-time conversation with the AI podcast host to learn more. The blueprint uses NIM microservices like Mistral-Nemo-12B-Instruct for language, Nvidia Riva for text-to-speech and automatic speech recognition, and the NeMo Retriever collection of microservices for PDF extraction. The AI Blueprint for 3D-guided generative AI gives artists finer control over image generation. While AI can generate amazing images from simple text prompts, controlling image composition using only words can be challenging. With this blueprint, creators can use simple 3D objects laid out in a 3D renderer like Blender to guide AI image generation. The artist can create 3D assets by hand or generate them using AI, place them in the scene and set the 3D viewport camera. Then, a pre-packaged workflow powered by the FLUX NIM microservice will use the current composition to generate high-quality images that match the 3D scene. Nvidia NIM microservices and AI Blueprints will be available starting in February. NIM-ready RTX AI PCs will be available from Acer, ASUS, Dell, GIGABYTE, HP, Lenovo, MSI, Razer and Samsung, and from local system builders Corsair, Falcon Northwest, LDLC, Maingear, Mifcon, Origin PC, PCS and Scan.

NVIDIA Supercharges RTX AI PCs with local foundation models

NVIDIA at the CES 2025 announced the launch of foundation models designed to run locally on NVIDIA RTX AI PCs, unlocking new possibilities for digital humans, content creation, productivity, and development. This move signifies a major shift, bringing the power of generative AI directly to the hands of users and solidifying NVIDIA's commitment to democratizing AI. The GeForce platform has long been a cornerstone for AI developers. The groundbreaking AlexNet, the first GPU-accelerated deep learning network, was trained on the GeForce GTX 580 in 2012. Last year alone, over 30% of published AI research papers cited the use of GeForce RTX, highlighting its enduring impact on the field. With the advent of generative AI and RTX AI PCs, NVIDIA is opening the doors to AI development for everyone. New low-code and no-code tools like AnythingLLM, ComfyUI, Langflow, and LM Studio empower enthusiasts to leverage complex AI models through intuitive graphical user interfaces. Further simplifying the process, NVIDIA is introducing NIM microservices. These will connect seamlessly with popular GUIs, providing effortless access and deployment of the latest generative AI models. NVIDIA AI Blueprints, built on NIM microservices, offer preconfigured workflows for various applications, including digital humans and content creation. To meet the burgeoning demand, every major PC manufacturer and system builder is launching NIM-ready RTX AI PCs. These microservices will also be integrated into NVIDIA Project DIGITS, a personal AI supercomputer powered by the new NVIDIA GB10 Grace Blackwell Superchip. Project DIGITS, featuring a petaflop of AI computing performance, will empower researchers, data scientists, and students to prototype, fine-tune, and run large AI models. Foundation models, the bedrock of generative AI, are neural networks trained on massive datasets. NVIDIA is set to release a comprehensive pipeline of NIM microservices for RTX AI PCs, featuring contributions from leading model developers like Black Forest Labs, Meta, Mistral, and Stability AI. This collection will encompass a wide range of use cases, including: NVIDIA also announced the Llama Nemotron family of open models, known for their high accuracy on diverse agentic tasks. The Llama Nemotron Nano model, available as a NIM microservice for RTX AI PCs and workstations, will excel in tasks like instruction following, function calling, chat, coding, and math. NIM microservices are optimized for deployment across NVIDIA GPUs, ensuring seamless performance whether running on RTX PCs, workstations, or in the cloud. Developers and enthusiasts can quickly download, set up, and run these NIM microservices on Windows 11 PCs with Windows Subsystem for Linux (WSL). They will be compatible with leading AI development and agent frameworks, including AI Toolkit for VSCode, AnythingLLM, ComfyUI, CrewAI, Flowise AI, LangChain, Langflow, and LM Studio. This compatibility allows developers to connect applications and workflows to AI models running NIM microservices through industry-standard endpoints, enabling a unified interface across various platforms. Enthusiasts will also be able to explore a range of NIM microservices through an upcoming release of the NVIDIA ChatRTX tech demo. To showcase the potential of NIM for building AI agents and assistants, NVIDIA previewed Project R2X, a vision-enabled PC avatar. This avatar can: NIM microservices will also be accessible through AI Blueprints, ready-to-use AI workflows designed for local execution on RTX PCs. These blueprints enable developers to create diverse applications, such as: NVIDIA NIM microservices and AI Blueprints will be available starting in February. NIM-ready RTX AI PCs will be available from leading manufacturers, including Acer, ASUS, Dell, GIGABYTE, HP, Lenovo, MSI, Razer, and Samsung, as well as various system builders. This announcement marks a significant leap forward in bringing the power of AI to a wider audience. By enabling local execution of foundation models on RTX AI PCs, NVIDIA is fostering a new era of AI-powered creativity, productivity, and development, limited only by the imagination of users and developers.