“Nvidia has history on its side.”
You couldn’t take 10 steps at CES, the annual Consumer Electronics Show in Las Vegas, without hearing about AI. All the key players in the AI PC race were present last week in force, including Intel
INTC,
AMD
AMD,
Qualcomm
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and Microsoft
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Intel made the most noise at the event by hosting a press conference and keynote to highlight its latest consumer chip, the Intel Core Ultra. Meanwhile, AMD released a pre-recorded “special address” to talk about its advancements in the AI PC space, including more details on its new Ryzen 8040 series of chips that also have an integrated network processing unit (NPU) for accelerating AI. AMD showed new comparative benchmarks for these chips against Intel’s Core Ultra product; as you would expect, the comparisons are favorable both in the AI results shown and the integrated-graphics performance, and attempt to dissuade the industry from thinking Intel’s latest chip is leaving AMD in the dust.
But since the “AI PC” mantra started sometime in the middle of last year, there has been one name curiously missing from the debate: Nvidia
NVDA,
The company is hoping to change that mindset by putting some serious muscle of its own into the marketing and positioning of AI on the PC. After all, Nvidia has history on its side: It was the hardware vendor that paved the road that the AI markets are all driving down today when it introduced CUDA, programmable graphics processing unit (GPU) chips, and created a software ecosystem rivaling anyone in the industry.
With Nvidia reaching its $1 trillion valuation on the back of the growth of the AI market for data centers — selling GPUs that cost tens of thousands of dollars to companies training the biggest and most important AI models — what does the company gain by spending effort in this AI PC race? The most basic benefit is selling more GPUs into the consumer space, for machines that don’t already have a separate GPU as part of the standard build. Gaming laptops and gaming PCs always have a graphics card in them, but more mainstream devices tend to exclude them for cost considerations. If Nvidia can make the case that any true AI PC has a GeForce GPU in it, that translates into more sales across the spectrum of price points.
Other benefits include maintaining Nvidia GPU chips as the bedrock that the next great AI application is built on, and convincing the industry and investors that the advent of the NPU isn’t going to lead to a fundamental shift in the AI computing market.
The Nvidia GPU angle for the AI PC is interesting due to their raw performance. While the integrated NPU on the Intel platform offers 10 TOPS (tera-operations per second, a standard way of talking about AI performance), a high-end GeForce GPU can supply more than 800 TOPS. Obviously, an 80x improvement in AI computing resources means that the capability to build innovative and revolutionary AI applications is a lot higher. Even mainstream discrete GPUs from Nvidia will offer multiple times more computing capability than this year’s NPUs.
And these kinds of GPUs are not just on desktop machines but are available in notebooks, too. This means that the laptop “AI PC” doesn’t need to be one powered by just an Intel Core Ultra, but can include a high-performance GPU for the most intense AI applications.
Graphics chips have been the basis for AI application development from the beginning, and even the generative AI push that has skyrocketed the popularity of AI in the consumer space runs best on Nvidia hardware today. Local Stable Diffusion tools that can create images from text prompts all default to utilizing Nvidia GPU hardware, but require careful tweaking and inclusion of specialized software modules to run effectively on Intel or AMD NPUs.
Nvidia had a couple of demos at CES that impressed and drove the point home of how it sees the world of AI on the PC. First was a service it worked on with a company called Convai to change how game developers create and how gamers interact with non-player characters in a game or virtual world. Essentially, the implementation allows a game dev to use a large language model like ChatGPT, adding in some bits and flavor about a character’s background, characteristics, and likes and dislikes, to generate a virtual character with a lifelike personality. A gamer can then talk into a microphone, having that speech converted to text via another AI model and sent to the game character like a modern AI-based chatbot — getting a response that is converted into speech and animation in the game.
I watched multiple people interact with this demo, challenging an AI character with different scenarios and questions. The solution worked amazingly well and incredibly quickly, enabling a real-time conversation with an AI character that is game- and context-aware. And this AI computing happens partially on the local gaming machine and its GPU, and partially in the cloud on a collection of Nvidia GPUs — truly a best-case scenario for Nvidia .
Another demo used the power of the GPU in a desktop system to build a personalized ChatGPT-like assistant, using an open-source language model and then simply pointing it to a folder full of personal documents, papers, articles and more. This additional data “fine-tunes” the AI model and allows the user to have a conversation or ask questions of the chatbot based on that data, which included personal emails and previous writings. It was only a tech demo and not ready for general release, but this is one of the promises that an AI PC addresses and, in this instance, it’s all running on a Nvidia GPU.
There are tradeoffs, of course. Most of the time, the Nvidia GeForce GPUs in a laptop or desktop system are going to use a lot more power than the integrated NPU on a chip like the Intel Core Ultra. But for AI work where you need an output quickly, the power of discrete GPUs is going to help you get that work done faster.
I have no doubts that AI is going to transform how we interact with and use our PCs for the better, and sooner than many believe possible. There will be a spectrum of solutions to enable this, from the low-power integrated NPU found on Intel, AMD and Qualcomm’s newest laptop chips, to the high-performance GPUs from Nvidia and AMD, as well as cloud and edge connected computing. All these options will be blended to provide the best consumer experiences. But discussions about the “AI PC” revolution on our doorstep without Nvidia is a pretty big miss.
Ryan Shrout is the President of Signal65 and founder at Shrout Research. Follow him on X @ryanshrout. Shrout has provided consulting services for AMD, Qualcomm, Intel, Arm Holdings, Micron Technology, Nvidia and others. Shrout holds shares of Intel.
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