Nvidia’s New AI Releases at CES 2025: Explored
The semiconductor industry's pivot towards AI has reached an inflection point in 2025, as manufacturers face increased pressure to deliver chips capable of running complex AI workloads on local devices rather than in the cloud.
It is no secret that this change is in response to growing enterprise demands for reduced cloud computing costs, enhanced data privacy and decreased latency in AI applications.
The trend towards edge computing and on-device AI processing has also sparked renewed competition among chipmakers.
While companies including Intel, AMD and Qualcomm have announced new AI-focused processors, industry leader Nvidia maintains its dominant position with an 80% share of the enterprise AI chip market.
Against this backdrop, Nvidia has announced Project DIGITS, a personal AI supercomputer, alongside developments in consumer graphics, robotics and autonomous vehicles at CES 2025.
Nvidia GeForce RTX 50 series marks shift to consumer AI
The company's GeForce RTX 50 Series graphics processing units (GPUs), built on the Blackwell architecture, contains 92 billion transistors and perform 3,352 trillion AI operations per second.
Now, we’re entering the era of “physical AI, AI that can proceed, reason, plan and act.”
The GPUs incorporate Deep Learning Super Sampling 4 (DLSS 4), a frame generation technology that uses AI to increase gaming performance.
“The latest generation of DLSS can generate three additional frames for every frame we calculate,” Jensen Huang, CEO of Nvidia, explains.
“As a result, we're able to render at incredibly high performance, because AI does a lot less computation.”
Meanwhile, for content creators, Nvidia has introduced AI foundation models for RTX PCs through Nvidia NIM microservices, which assist in creating digital content.
“We're creating a whole bunch of blueprints that our ecosystem could take advantage of. All of this is completely open source, so you could take it and modify the blueprints,” Jensen says.
Nvidia Cosmos platform targets autonomous vehicle market
The company's Cosmos platform integrates AI models and processing pipelines for autonomous vehicles and robots.
Uber has adopted the technology, which enables physical AI systems to process environmental data.
- Cosmos platform advances physical AI in robots, autonomous vehicles and vision AI
- GeForce RTX 50 Series GPUs with the RTX 5090 features 92 billion transistors and 3,352 trillion AI operations per second
- AI foundation models for RTX PCs feature Nvidia NIM microservices for creating digital humans, podcasts, images and videos
- AI Blueprints for agentic AI integrates with platforms from providers like CrewAI and LangChain
Jensen suggests this development could mark a watershed moment for robotics technology: “The ChatGPT moment for general robotics is just around the corner.”
Furthermore, the DRIVE Hyperion autonomous vehicle platform, built on Nvidia's AGX Thor system-on-a-chip, supports advanced driving capabilities.
“Building autonomous vehicles, like all robots, requires three computers: Nvidia DGX to train AI models, Omniverse to test drive and generate synthetic data and DRIVE AGX, a supercomputer in the car,” Jensen says.
The platform's synthetic data capabilities enable significant scaling of autonomous vehicle training.
Jensen notes that Nvidia's AI data factory can convert “hundreds of drives into billions of effective miles” using Omniverse and Cosmos technologies.
Enterprise AI solutions focus on manufacturing sector
Nvidia has partnered with CrewAI and LangChain to launch AI Blueprints for enterprise workflows.
The blueprints integrate with Nvidia AI Enterprise software to enable custom AI agent development.
“Developers can use Nvidia NIM microservices to build AI agents for tasks like customer support, fraud detection and supply chain optimisation,” Jensen notes.
The Isaac GR00T Blueprint for synthetic motion generation targets manufacturing applications, using Nvidia's Omniverse platform to generate training data for humanoid robots.
This development represents a shift from perception AI, which focuses on understanding images, words and sounds, to physical AI systems capable of processing, reasoning, planning and acting.
The technology progression traces the evolution of AI from basic perception tasks through generative capabilities to physical world applications.
“All of the enabling technologies that I've been talking about are going to make it possible for us in the next several years to see very rapid breakthroughs, surprising breakthroughs, in general robotics,” Jensen concludes.
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