Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Cohere launched an early access program for North, an AI platform designed to make daily work more efficient. The aim for Cohere North is to combine large language models, search and agents in one secure enterprise work and collaboration platform that can run anywhere.

With the move, Cohere, which was founded in 2019, is downplaying the LLM arms race yet could wind up being more effective for enterprise customers. The company refers to its North strategy as one focused on "meaningful AI. Other foundational model players are also working to diversify. Anthropic is adding collaboration features as it expands use cases for its Claude models. OpenAI is also expanding but has largely focused on expanding into search to compete with Google.

• Agentic AI: Three themes to watch for 2025
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The upshot here is that foundational model giants are going to have to surround their LLMs with applications. Cohere will have its models but is making a push to be an AI workspace platform to improve the speed and quality of everyday tasks. Cohere in July raised $500 million in funding for a $5.5 billion valuation and now has a more rounded offering.

According to Cohere, North will combine the following:

• Cohere models with other frontier models.
• Search discovery and automation.
• A multimodal AI search system called Compass to extract information from images, slides, spreadsheets and documents.
• Enterprise-grade security and privacy standards.
• A turnkey approach designed to speed up time to value relative to do-it-yourself approaches.
• The ability to be deployed in public and private clouds as well as on premises.

Cohere North, which is taking enterprise applications for early access, is a bet that AI returns are directly tied to workforce adoption. If teams have an easier way to use AI for everyday tasks, they're more likely to adopt it. Royal Bank of Canada and Cohere have partnered to co-develop North for Banking, a platform connects RBC's procedure and policy documents and processes with AI models.

In the blog post, Cohere said North is "optimized to run in private–including air-gapped–environments so that organizations can safely integrate all their sensitive data in one place." Cohere added that its vertically integrated stack will enable North to enable AI agents to perform complex tasks across enterprise silos.

Cohere is also taking aim at the retrieval augmented generation performance of much larger players such as Microsoft Copilot and Google Vertex AI.

What's next? Cohere appears to be going vertical. Cohere CEO Aiden Gomez said in a post:

"Since we developed each part of the technology stack underpinning North, the platform can be tailored to suit the unique needs of any business. This granular level of control is essential for customizing AI solutions to match each organization's needs such as industry-specific terminology and internal knowledge. Additionally, with our industry-leading focus on privacy and security, North is well suited for regulated industries where companies simply cannot risk their proprietary data."

In other words, the RBC partnership will likely be replicated across other industries.

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Retailers are about to get swamped with AI agents that are designed to boost efficiency and enhance customer experiences if news out of NRF 2025 is any indicator.

NRF 2025 in New York has a heavy dose of AI across the supply chain to distribution to customer experiences. Adobe in its holiday shopping report noted that consumers embraced generative AI chat bots as customer assistants and led to a 1,300% increase in traffic to retail sites.

IBM in a recent study found that retail and consumer product companies are shifting spending toward AI. IBM found that retail and consumer product companies will allocate an average of 3.32% of their revenue to AI to enhance customer service, supply chain and marketing.

With that backdrop, here's a roundup of what's being pitched to retailers for 2025.

Salesforce launched Agentforce for Retail with features that couple in-store and digital shopping. Agentforce for Retail includes new prebuilt skills for order management, guided shopping, appointment scheduling and marketing skills for loyalty promotion creation. Salesforce also launched Retail Cloud with Modern POS. 

Google Cloud launched Agentspace for Retailers, which gives retailers one place to combine the company's agentic AI tools, Gemini models and search. In addition, Google Cloud outlined Vertex AI Search for Commerce and Google Cloud Gen AI Catalog and Content Enrichment. Google also said it would bring its Vision AI to physical retailers via a partnership with Everseen Partners. Also: Google Cloud launches Agentspace to create, deploy agents

Oracle launched the latest version of its Xstore Point of Service, which is updated with a new interface and workflows with analytics for store associates. Xstore also has a new architecture built on Oracle Cloud Infrastructure and Oracle Autonomous Database. The system can be deployed on various flavors of cloud infrastructure or on-premises. 

HPE launched new HPE Aruba Networking gear for retailers including the HPE Aruba Networking 100 Series Cellular Bridge for wide area networking access, the HPE Aruba Networking CX 8325H switch for compact spaces and HPE Aruba Networking 750 Series wireless access points. HPE also launched a new management console designed to optimize retail indoor and outdoor networks and detect Internet of things anomalies. 

Blue Yonder launched its December platform update that features "AI-centric data modeling." Blue Yonder added integrated demand and supply planning with AI and machine learning updates to combine supply chain, financial operations and risk and opportunity scenarios. A few items in the December release include:

• One view of online and in-store inventory.
• Support for vertically integrated retailers.
• Enhanced returns orchestration with integrations between warehouse management, commerce and returns management.
• AI tools added across the platform to cover inventory optimization and planning for demand, business and supply chain.

Talkdesk announced Talkdesk AI Agents for Retail, which aims to bring agentic AI to retail customer service. These conversational agents are designed to handle retail processes autonomously and take action.

Crisp, a company that specializes in retail analytics, launched AI Blueprints, a suite of open-source AI templates for consumer product group companies. The AI Blueprints aim to automate manual tasks, simplify processes and harmonize data. The AI Blueprints are free and run within Databricks and Google Cloud with Snowflake on deck.

Planalytics announced Xtreme, a set of applications that uses AI to forecast consumer demand to significant weather events such as hurricanes, flooding, heat waves and cold snaps. Planalytics also co-authored a report with NRF on climate proofing retail.

Zebra Technologies outlined the Zebra Mobile Computing AI Suite, a set of development tools to bring AI vision to Zebra Android Devices. Zebra also said the AI tools integrated into Zebra Companion, a suite of AI agents designed for retail frontline workers.

More retail:

• Lowe's betting on AI to drive customer experience, optimize multiple processes
• How Home Depot blends art and science of customer experience
• Scotts Miracle-Gro and UserTesting: 9 customer experience takeaways
• Walmart, Target highlight intersection of supply chain, customer experience
• SoundCommerce CEO Eric Best: 'Data capability enables customer lifetime value'
• Shopify increasingly becoming enterprise commerce OS, replaces incumbents
• Walmart's big tech, AI bets paying off going into 2024 holiday shopping
• Sam's Club CEO Nicholas on AI, frictionless commerce, focus on members
• How Wayfair's tech transformation aims to drive revenue while saving money

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Nvidia CEO Jensen Huang said useful quantum computers are more than a decade away, Project Digits fills a big void for data scientists and developers and serves as a personal AI cloud, and human robotics will develop faster than expected.

Huang fielded financial analyst questions at CES 2025 and generated a few headlines worth noting. From a stock market perspective, Huang managed to tank quantum computing stocks, which have had a torrid run over the last two months.

We'll start with Huang's quantum comments, but in the near term the comments on robotics and Project Digits was arguably more actionable. Nvidia had a busy CES 2025 with agentic AI developments and Project Digits, which puts an AI supercomputer on a desktop. The upshot is AI inference and development may become distributed.

• Nvidia launches Project Digits, a desktop AI supercomputer
• Nvidia launches Cosmos models, aims to expand physical AI, industrial reach
• Nvidia moves to advance agentic AI use cases at CES 2025

Here's what Huang had to say:

Quantum computing strategy. Huang was asked about quantum computing and its usefulness in the near-term. Huang said that quantum computing can't solve every problem and that the technology is "good at small data problems."

"The reason for that is because the way you communicate with a quantum computer is microwaves. Terabytes of data is not a thing for them. There are some interesting problems that you could use quantum computers like generating a random number cryptography...

We're not offended by anything around us, and we just want to build computers that can solve problems that normal computers can't.

Just about every quantum computing company in the world is working with us now, and they're working with us in two ways. One, is quantum classical and we call it CUDA Q. We're extending CUDA to quantum and they use us for simulating the algorithms, simulating the architecture, creating the architecture itself.

Someday we'll have very useful quantum computers. We're probably five or six orders of magnitudes away, 15 years for useful quantum computers and that would be on the early side. 30 years is probably on the late side. If you picked 20 years a whole bunch of us would believe it. We want to help the industry get there as fast as possible and create the computer of the future."

• Practical quantum computing advances ramp up going into 2025
• Quantum computing all in on hybrid HPC with classical computing

Constellation Research analyst Holger Mueller said:

"It is no surprise Nvidia works with all Quantum vendor to help offload, prepare and operate workloads and data for quantum machines. Huang is a little off with the use case benefits. In Spring 2023 some quantum vendor showed and used real enterprise workloads. But he is right - the viability is limited. This may change in 2025 though - where all eyes are on IBM and there plan to couple multiple Heron systems." 

Blackwell demand. Huang said Hopper and Blackwell demand is strong on a combined basis.

The Blackwell system on a chip (SOC) in Project Digits. Nvidia worked with MediaTek on the Blackwell SOC and Huang was asked why the company didn't do the architecture itself. "MediaTek does such as good job building lower power SOCs," said Huang. "If we can partner to do something then we can do something else. MediaTek did a wonderful job. We shared our architecture with them and it was a great win-win and saved a lot of engineering."

Project Digits market. Huang was asked about Project Digits and Nvidia's PC ambitions. He said:

"I'm getting incredible emails from developers about Digits. There's a gaping hole for data scientists and ML researchers and who are actively building something and you don't need a giant cluster. You're just developing the early versions of the model, and you're iterating constantly. You could do it in the cloud, but just costs a lot more money. Personal computing exists so you can have a op-ex, capex trade off. A lot of developers are trying to get through using Macs or PCs and now they have this incredible machine sitting next to them. You could connect it through USB-C, or you could connect it using LAN, or Wi-Fi. And now it's sitting next to you. It's your personal cloud, and it runs the full stack of what you run on DGX. Everything runs exactly the same way.

"It's essentially your own private cloud. If you would like to have your own AI assistant sitting on this device, you can as well. It's not for everyone, but it's designed for data scientists, machine learning researchers, students."

What's the PC plan? Nvidia said Project Digits is focused but Huang wasn't going to say much about future PC efforts. "Obviously we have plans," he said.

The platform. Huang emphasized that Nvidia is building a platform for "every developer and every company." He said Nvidia can extend into World Foundation Models, robotics and computational lithography for TSMC. "We love the concept of a programmable architecture," he said.

Automotive and autonomy. "Every car company in the world will have two factories, a factory for building cars and the factory for updating their AIs," he said. "Every single car company will have to be autonomous, or you're not going to be a car company. Everything that moves will be autonomous."

Robotic systems and strategy. "There are no limitations to robots. It could very well be the largest computer industry ever," said Huang. "There's a very serious population and workforce situation around the world. The workforce population is declining, and in some manufacturing countries it's fairly significant. It's a strategic imperative for some countries to make sure that robotics is stood up."

"Human robots will surprise everybody with how incredibly good they are," said Huang.

Analysts also asked Huang about the robotic strategy. He said:

"Our robotic strategy for automotive and robot and human robots, or even robotic factories, are exactly the same. It is technologically exactly the same problem. I've generalized it into an architecture that can address a massive data problem."

AI assistants for coding. "If a software engineer is not assisted with an AI, you are already losing fast. Every software engineer at Nvidia has to use AI assistance next year. That's just a mandate," he said.

What models Huang uses. Huang said he uses OpenAI’s ChatGPT the most followed by Google’s Gemini models, especially the ones aimed at deep research. “Everybody should use these, Ais at least as a tutor,” said Huang. “Every kid should use AIs as a tutor. The continuous dialog is insanely good and it’s going to get better.”

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Paychex said it will acquire Paycor HCM in an all-cash deal valued at $4.1 billion.

Under the terms of the deal, Paycor shareholders will get $22.50 a share. The deal is expected to close in the first half of 2025.

The merger brings together two Constellation Research Shortlist vendors for payroll for North American SMBs and HCM suites focused on North America.

According to Paychex and Paycor, the combined company will boost its total addressable market by offering a suite of technology and services for HR, employee benefits, insurance and payroll.

Paycor has 2,900 employees and more than 49,000 customers. Paycor's HCM, payroll and talent software has typically served small businesses but has been moving toward larger enterprises.

John Gibson, CEO of Paychex, said Paycor "will enhance our capabilities upmarket, broaden our suite of AI-driven HR technology capabilities, and provide new channels for sustained long-term growth."

The combined company is expecting cost savings of more than $80 million in the short term and neutral to accretive to adjusted earnings in the first year post close. Paychex will gain from Paycor's partnerships and sales coverage and HCM features that will round out the HCM suite.

Going forward, Paychex and Paycor will invest in data and AI tools for customers.

Constellation Research analyst Holger Mueller said:

"There always have been consolidation trends in the HCM market – but the acquisition of Paycor by Paychex is a new apex. The SMB player buys the large enterprise player, resulting in a double automation portfolio – that is unrivaled – apart from UKG, which itself is the product of a merger. The acquisition is good news for the higher-end Paychex customer and for Paycor customers who have smaller operations and subsidiaries in Canada. It is now up to the Paychex management team to successfully integrate portfolios, go to market and more."

In a presentation, the focus of the deal was go-to-market efforts. Paychex and Paycor expect to win in more customer segments, sell Paycor's workforce management, talent acquisition and talent management tools to Paychex customers. Paychex's HR advisory and employee solutions will be sold to Paycor customers.

By the numbers:

• Paychex has $5.3 billion in annual revenue.
• Paychex has more than 2.3 million HR outsourcing worksite employees.
• Paychex HCM and payroll software has processed more than $950 billion financial transactions and is the leading 401(k) record keeper in the US.
• Paychex has 745,000 payroll clients.
• Paycor has $655 million in annual revenue and 90% of it is recurring revenue.
• Paycor revenue growth in fiscal 2024 was 19%.
• Paycor has 2.7 million active employees on its platform.

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

The launch of Nvidia's Project Digits, a desktop AI supercomputer starting at $3,000 for data scientists and AI researchers, is just one example of a broader theme: AI training and inferencing is going to be decentralized to some degree.

Nvidia's news--delivered by CEO Jensen Huang at CES 2025--highlights a key use case revolving around offloading AI development with a high-powered system. The general idea is that users can develop and run inference on models on the desktop and then deploy to the cloud or data center.

For enterprises pondering on-premises data centers and wrestling with cloud costs, Nvidia's Project Digits isn't a bad idea--especially when you can network two of those systems together to run up to 405-billion parameter models.

"With Project Digits, developers can harness the power of Llama locally, unlocking new possibilities for innovation and collaboration," said Ahmad Al-Dahle, Head of GenAI at Meta.

Constellation Research analyst Holger Mueller said:

"Digits poses lots of challenges, but if Nvidia overcomes them it has repercussions across the whole AI stack. Bigger has been better for AI and the question has always been this: Is it cost effective and can it be run on remote locations? Digits ends that discussion. It is Blackwell everywhere (and with that the soon to be announced Blackwell successor) as well."

But Nvidia's desktop supercomputer is just part of the mix.

Dell Technologies said it will be among the first to roll out systems powered by AMD's latest Ryzen processors. AMD launched Ryzen AI Max Pro, Ryzen AI 300 Pro and AMD Ryzen 200 Pro Series processors for business PCs. HP also outlined the HP ZBook Ultra G1a 14 mini workstation.

With the move, AMD is putting high-performance computing into thin laptops and looking to capture AI workloads on workstations. Ryzen AI Max Pro Series processors are designed for large engineering and models.

Simply put, there should be enough AI compute in the field that can be networked and leveraged. 

Intel also updated its Intel Core lineup with a focus on business AI applications. For good measure, Qualcomm outlined its Snapdragon X series, which will bring AI and Copilot+ PCs to the masses at a price around $600.

• Santa isn't bringing you an AI PC in 2024
• PC industry's big dream: AI enabled PCs spur upgrade cycle
• PC upgrade cycle to be driven by AI, but calling timing has been difficult

How will this develop?

These systems at CES could be construed as yet another effort to prod consumers and businesses to upgrade PCs. The AI PC upgrade cycle will happen, but it has clearly been delayed. Instead, look upstream to Project Digits, which will have a real AI workload purpose. Yes, enterprises will be more dependent on Nvidia but being able to develop and tweak models locally and then use data center resources is likely to be cheaper and arguably more sustainable.

With all the cool kids--data scientists and AI wonks--buying supercomputer workstations the rest of us will likely boost specs just to have the horsepower. Of course, the masses won't use that horsepower, but if a savvy enterprise can capture the compute at the edge there's real returns ahead.

There's already signs that enterprises are starting PC upgrades and more future-proof AI friendly systems are likely to seal the deal. For consumers, the AI PC upgrade cycle will be slower, but you can expect Qualcomm to capture some real share.

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Nvidia CEO Jensen Huang said the company is aiming to expand use cases for physical AI including next-generation robots and autonomous vehicles with the launch of world foundation models called Cosmos.

Cosmos is a family of world foundation models, or neural networks that can predict and generate physics-aware virtual environments. Speaking at the headline keynote at CES 2025, Huang said Nvidia Cosmos models will be open sourced. "The ChatGPT moment for robotics is coming. Like large language models, world foundation models are fundamental to advancing robot and AV development," said Huang. "We created Cosmos to democratize physical AI and put general robotics in reach of every developer."

• Nvidia moves to advance agentic AI use cases at CES 2025

Huang added that world foundation models (WFMs) will be as important as large language models, but physical AI developers have been underserved. WFMs will use data, text, images, video and movement to generate and simulate virtual worlds that accurately models environments and physical interactions. Nvidia said 1X, Agility Robotics and XPENG, and autonomous developers Uber, Waabi and Wayve are among the companies using Cosmos.

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The Cosmos launch is part of a wide range of models Nvidia outlined to bolster its ecosystem NeMo framework and platforms like Omniverse and AI Enterprise. Nvidia also launched Nvidia Llama Nemotron and Cosmos Nemotron models for developers creating agentic AI applications.

Huang said WFMs are costly to train. The best way to democratize physical AI was to spend upfront on a suite of open diffusion and autoregressive transformer models for physics-aware video generation and then open them so developers can refine. Cosmos models have been trained on 9,000 trillion tokens from 20 million hours of real-world human interactions, environment, industrial, robotics and driving data.

Cosmos can process 20 million hours of data in just 40 days on Nvidia Hopper GPUs, or 14 days on Blackwell GPUs.

Huang showcased Cosmos models capabilities such as video search and understanding, 3D-to-real synthetic data generation, physical AI model development and evaluation, the ability to predict next potential actions and simulations.

Cosmos WFMs are available on Hugging Face and Nvidia's NGC catalog. Cosmos models will soon be optimized for Nvidia NIM microservices.

Key details about Cosmos include:

• The models come in three categories. Cosmos Nano is optimized for low-latency inference and edge deployments. Super is for baseline models and Ultra is designed for maximum quality and best for distilling custom models.
• Cosmos is designed to be paired with Nvidia Omniverse 3D outputs.
• Developers can use Cosmos models for text-to-world and video-to-world generation with versions ranging from 4 billion to 14 billion parameters.
• Cosmos WFMs can enable synthetic data generation to augment training datasets and simulate, test and debug physical AI models before being deployed.
• Data curation and training of Cosmos used thousands of Nvidia GPUs via Nvidia DGX Cloud.
• Cosmos will be supported via Nvidia AI Enterprise.
• Nvidia said the Cosmos platform includes data processing and curation for Nvidia NeMo Curator.
• Cosmos will include Cosmos Guardrails and will have a built-in watermarking system.

Industrial AI applications

The launch of Cosmos combined with Nvidia Omniverse is designed to accelerate industrial AI applications. These applications--notably factory and manufacturing optimization, robotic digital twins and autonomous applications--are the next era of AI.

To that end, Nvidia is leveraging partners such as Accenture, Altair, Ansys, Cadence, Foretellix, Microsoft and Neural Concept to integrate Omniverse into software and services. Siemens also announced that its Teamcenter Digital Reality Viewer is available and powered by Nvidia Omniverse.

Huang said:

"Physical AI will revolutionize the $50 trillion manufacturing and logistics industries. Everything that moves — from cars and trucks to factories and warehouses — will be robotic and embodied by AI."

Nvidia's plan is to pair Cosmos with Omniverse and its DGX platform to generate synthetic data and then combine it with blueprints and agents.

Nvidia outlined a set of Omniverse Blueprints including;

• Mega, which is for developing and testing robot fleets at scale in a factory or warehouse digital twin before real-world deployment.
• Autonomous Vehicle simulation for AV developers can replay driving data, generate ground truth data and closed loop testing.
• Omniverse Spatial Streaming to Apple Vision Pro for enterprise applications.
• Real-Time Digital Twins for Computer Aided Engineering, a reference workflow.

Nvidia also launched the Isaac GR00T Blueprint to accelerate humanoid robotics development. Nvidia Isaac GR00T is designed for synthetic motion generation that can train robots using imitation learning. The workflow captures human data and then multiplies it with synthetic data.

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Nvidia launched new models for agentic AI called Nemotron, expanded its set of blueprints and expanded its ecosystem for orchestration.

The news, part of a barrage of items at CES 2025, was announced by Nvidia CEO Jensen Huang. Nvidia's CES news featured Project Digits, a desktop AI supercomputer, and a broad effort to tackle physical AI use cases, but the company continues to build on its AI agent strategy.

• Nvidia launches Cosmos models, aims to expand physical AI, industrial reach

Nvidia launched the Llama Nemotron family of models that are designed to help developers create and deploy agents across use cases including customer support, fraud detection, supply chain and inventory management.

• Nvidia launches NIM Agent Blueprints aims for more turnkey genAI use cases
• Agentic AI: Three themes to watch for 2025
• Agentic AI without process optimization, orchestration will flop

The company also launched the Nvidia Cosmos Nemotron visual language models and Nvidia NIM microservices for video search and summarization. With the Cosmos-Nemotron connection, Nvidia is looking to enable developers to build agents that can analyze and respond to images and video from autonomous machines, hospitals, stores and warehouses.

Key items include:

• Nvidia Llama Nemotron models are trained with Nvidia's latest technology, agentic capabilities and quality data sets. These models are designed to excel at following instructions, chat, coding and math. SAP and ServiceNow are early customers of the Llama Nemotron models.
• Llama Nemotron models come in three sizes. Nano is 4B parameters, Super is 49B and Ultra is 253B.
• The new models are available as hosted APIs from Nvidia and Hugging Face.
• Nvidia also outlined new agentic AI blueprints that include NIM microservices, NeMo and agentic AI frameworks from CrewAI, Daily, LangChain, LlamaIndex and Weights & Biases. Those AI frameworks are designed to orchestrate and manage agentic AI and can be used for software development, speech recognition, report generation and research.
• Accenture also said it will launch more than 100 AI Refinery agents for industries using Nvidia's stack.

Nvidia also launched a new AI Blueprint for PDF to podcast. Other Nvidia blueprints were focused on Cosmos models and physical AI.

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Nvidia is launching a desktop AI supercomputer called Project Digits aimed at AI researchers, data scientists and students. Systems, available in May, will start at $3,000 and include a Nvidia Grace Blackwell Superchip that is capable of running 200B parameter models.

The use case for Digits is to offload compute for AI development from cloud resources as needed. Project Digits will be available from Nvidia and partners with DGX and a Linux operating system. Nvidia is designed for AI development and a complement to laptops for now.

According to Nvidia, the idea is that users can develop and run inference on models on the desktop and then deploy to the cloud or data center. Project Digits has a small form factor that's about as wide as a coffee mug including the end of the handle.

• Nvidia moves to advance agentic AI use cases at CES 2025
• Nvidia launches Cosmos models, aims to expand physical AI, industrial reach
• AI PCs may decentralize inferencing workloads

Jensen Huang, CEO of Nvidia, said Digits will democratize access to Grace Blackwell Superchips. "Placing an AI supercomputer on the desks of every data scientist, AI researcher and student empowers them to engage and shape the age of AI," said Huang.

He quipped that Project Digits is a placeholder name and if anyone has a better name to reach out to Nvidia.

Specs for Nvidia Digits include:

• Nvidia GB10 Grace Blackwell Superchip, which includes a Nvidia Blackwell GPU, latest generation CUDA cores and fifth-gen Tensor Cores connected via NVLink chip to chip interconnect to a Nvidia Grace CPU.
• 1 petaflop of AI performance.
• 128GB unified system memory.
• Up to 4TB NVMe storage.
• Nvidia's AI software stack including Nvidia Blueprints and NIM microservices.
• Compact form factor.
• Use cases for digits include prototyping, fine tuning, inference, data science and edge applications including robotics.

Using Nvidia ConnectX, the company said two Project Digits systems can be networked to run up to 405-billion parameter models. If Project Digits is successful, generative AI compute could become more distributed and lower costs. 

Constellation Research analyst Holger Mueller said:

"Nvidia is chasing revenue on both ends – the high end and the low end. That is not a unique strategy in the semiconductor space – but it is more stretched out over 5-10 years. Nvidia is trying to do this in under one year that Blackwell. Project Digits marks the second time Nvidia departs from semiconductor industry best practice. The other time was when revving platforms every year – instead of the two year cycle. Digits poses lots of challenges, but if Nvidia overcomes them it has repercussions across the whole AI stack. Bigger has been better for AI and the question has always been this: Is it cost effective and can it be run on remote locations? Digits ends that discussion. It is Blackwell everywhere (and with that the soon to be announced Blackwell successor) as well."

Nvidia executives said that Project Digits doesn't mean it's going into the PC market, but it could be a safe bet in the future. 

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