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What are AI PCs that Nvidia's Jensen Huang is betting on?

hist78 said:
$4,000 or $5,000 for an N1 or N1X computer may be too expensive for individual consumers, but for large corporate users, it could be justifiable and affordable because of the potential AI capabilities and productivity gains.

I know some major financial firms are deploying AI extensively. Two friends of mine who work in the financial industry told me that they don't know how they could perform their jobs with the same level of efficiency without AI.
Click to expand...

Yes definitely agreed it's indispesable for some use cases, but the models doing the real work are far beyond the capability of even N1X -- they're trillion+ parameter models requiring terabytes of RAM to run. That's why I'm curious what this is going to do.

I'm a huge hardware nerd - and want to see this useful for something, but I'm honestly struggling to see where it's going to help. For 'strong home AI' - Apple likely offers better value/$ at this stage. (CUDA is not required to run models locally). AMD also has some really decent offerings with "Strix Halo". And then on the higher end - servers and GPUs are readily available.

I think 'toe in the water' is probably an accurate take on this for now..
 
The frontier models may still be in that range, but smaller models are becoming quite good. I was very impressed by the performance of GPT-OSS 20B, which is pretty dated at this point. I believe you can find rather good coding agents that will fit into 128GB of memory.

Not sure about Apple being better value. Their 128GB products appear to be in the $5000+ range.
 
Xebec said:
I'm a huge hardware nerd - and want to see this useful for something, but I'm honestly struggling to see where it's going to help. For 'strong home AI' - Apple likely offers better value/$ at this stage. (CUDA is not required to run models locally). AMD also has some really decent offerings with "Strix Halo". And then on the higher end - servers and GPUs are readily available.
Click to expand...
If I look at where all the previous NVIDIA SPARK boxes have gone, it’s primarily universities, startups (like OpenAI was once) and in-house AI developers in enterprises. What do all of those have in common - research and development leveraging the NVIDIA ecosystem, without having to live inside the limitations of neoclouds / cloud providers.

I do think this product services a different market than the high end Macs or x86/Strix, that don’t have access to all the NVIDIA libraries. But I do wonder how much new TAM Windows brings over SPARK w Linux. Or maybe I’m wrong about who will buy.
 
KevinK said:
If I look at where all the previous NVIDIA SPARK boxes have gone, it’s primarily universities, startups (like OpenAI was once) and in-house AI developers in enterprises. What do all of those have in common - research and development leveraging the NVIDIA ecosystem, without having to live inside the limitations of neoclouds / cloud providers.

I do think this product services a different market than the high end Macs or x86/Strix, that don’t have access to all the NVIDIA libraries. But I do wonder how much new TAM Windows brings over SPARK w Linux. Or maybe I’m wrong about who will buy.
Click to expand...

Because NVIDIA's N1 and N1X will support both Linux and Microsoft Windows, their addressable market is much larger than it would be with Linux only support.
 
KevinK said:
If I look at where all the previous NVIDIA SPARK boxes have gone, it’s primarily universities, startups (like OpenAI was once) and in-house AI developers in enterprises. What do all of those have in common - research and development leveraging the NVIDIA ecosystem, without having to live inside the limitations of neoclouds / cloud providers.

I do think this product services a different market than the high end Macs or x86/Strix, that don’t have access to all the NVIDIA libraries. But I do wonder how much new TAM Windows brings over SPARK w Linux. Or maybe I’m wrong about who will buy.
Click to expand...

Do you remember SUN SPARC workstations. They never took off exactly because of a too elitarian, academic spin around them.

An average high end user was never explained why he had to choose it over just a faster x86 box.

Most powerful users don't care of "advanced capabilities," and niche features, as surprisingly as it sounds. They care for it being fast, over it being "advanced"

There is a whole genre of Chinese hardware dedicated for desktopifying old server parts sold at rock bottom prices, that still have amazing cost-performance ratio.
 
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floppydisk said:
The frontier models may still be in that range, but smaller models are becoming quite good. I was very impressed by the performance of GPT-OSS 20B, which is pretty dated at this point. I believe you can find rather good coding agents that will fit into 128GB of memory.

Not sure about Apple being better value. Their 128GB products appear to be in the $5000+ range.
Click to expand...
That's fair - the Apple priicng has gone up a lot. But you get a whole portable computer for that and a fully working OS .. (Vs Windows on ARM). The CPU performance is also significantly higher on the Apple side, too.. I suspect Nvidia has a bit of an advantage on GPU though.

Strix Halo 128GB can be had for about $3,000.
 
hist78 said:
Because NVIDIA's N1 and N1X will support both Linux and Microsoft Windows, their addressable market is much larger than it would be with Linux only support.
Click to expand...
To be fair - ARM Windows still has a lot of caveats. Printer Drivers, odd performance on certain apps, compatibility with certain apps..
 
I can see a lot of use cases for edge inference, but these seem most likely going into specific devices like cars, cameras, glasses, appliances.
I saw someone had mentioned there is academic use/preference. But for the general public, be it consumer or enterprise, I fail to see where the demand is for "local" general purpose inferencing.
 
Paul2 said:
Most powerful users don't care of "advanced capabilities," and niche features, as surprisingly as it sounds. They care for it being fast, over it being "advanced"
Click to expand...
I'm thinking that these users will care whether these kinds of libraries are available:

developer.nvidia.com

CUDA-X

Get higher performance with a set of GPU-accelerated libraries, tools, and technologies.
developer.nvidia.com developer.nvidia.com
 
hist78 said:
The new Nvidia RTX Spark N1 and N1X are obviously too expensive and unnecessary for most mainstream PC users. However, in terms of building a native developer network (rather than relying on x86 translation) and penetrating client environments, they represent a measured starting point. Starting without mass market volume can be a disadvantage, but it also allows Nvidia and MediaTek to cultivate their own market within a smaller and more controllable audience, such as edge AI and client AI developers.

Gaining developers' support is the first step toward building Nvidia's long term client hardware ecosystem. Developers come first then mass market adoption follows.
Click to expand...
I've been thinking, Nvidia is on a similar route to Apple taking more control in the design of the processors and having more "arm" over their systems. Apple has their own developer system, parted with x86 and is on its way to replace communication silicon, and has an OS to gel it all together, albeit in a different type of end product for different people.

Is Nvidia's own OS even worth considering? Something that integrates the most out of their distinguished CUDA library and has more control over their hardware than ever before.
 
bilau said:
I saw someone had mentioned there is academic use/preference. But for the general public, be it consumer or enterprise, I fail to see where the demand is for "local" general purpose inferencing.
Click to expand...
I see these new SPARK PCs as being the next generation of developer platforms for AI projects like this.

Building a team of AI agents for the next wave of cosmology data - Stockholms universitet

www.su.se www.su.se

There are literally thousands of these projects going on today, but I’m sure NVIDIA wants to scale to hundreds of thousands to millions.
 
KevinK said:
I see these new SPARK PCs as being the next generation of developer platforms for AI projects like this.

Building a team of AI agents for the next wave of cosmology data - Stockholms universitet

www.su.se www.su.se

There are literally thousands of these projects going on today, but I’m sure NVIDIA wants to scale to hundreds of thousands to millions.
Click to expand...
1780875255926.jpeg
 
bilau said:
But why must they run it locally? Token cost? Proprietary LLM? Connectivity? Need to keep work secret?
Click to expand...

All of them and probably more. See Jensen Huang's Q&A session at GTC Taipei last week.

hist78

Thread 'Nvidia GTC Taipei 2026 Financial Analyst Q&A'

Somehow, the video of the financial analyst Q&A session from last week's GTC Taipei is currently offline on Nvidia official website so I provided a replay link from a third-party YouTube channel.

Jensen Huang explained how Nvidia intends to position its various products, such as RTX Spark and Vera Rubin, in the market.

  • Like
 
freshshine1 said:
I've been thinking, Nvidia is on a similar route to Apple taking more control in the design of the processors and having more "arm" over their systems. Apple has their own developer system, parted with x86 and is on its way to replace communication silicon, and has an OS to gel it all together, albeit in a different type of end product for different people.

Is Nvidia's own OS even worth considering? Something that integrates the most out of their distinguished CUDA library and has more control over their hardware than ever before.
Click to expand...

It's probably too late and too complicated for Nvidia to enter the client operating system market, especially considering the huge number of existing applications written for Windows and Mac OS.

That's why Microsoft is a good fit for the Nvidia–MediaTek–Microsoft partnership. Additionally, MediaTek is strong in mobile devices/smart devices (smartphones, smart TVs, etc.), and wireless communications (5G, 6G, Wi-Fi). These are necessary building blocks for edge AI and physical AI.
 
"A SPARC computer is not a PC, it is built for completely different tasks" – I recall SUN execs were talking something along those lines.

SPARCs had countless features, and unique capabilities, about which no one was caring about, except the their marketing people.

SPARCs were definitely "unique," "bespoke," "used on Wall St.," and had quad precision arithmetic, which everyone knew was cool, but no one knew how to use, and even less so for what, and why... But plain Pentiums 3 were sold for 10 times cheaper, and were almost as fast.

The marketing theory as per business school: "add an exclusive feature A to product B, which rich clients need, then sell it for 10x the price to rich clients."

Here they took "rich advanced users," and are trying to sell them on "AI features" they think those advanced users desperately need, and who will spend 10x the price of a regular PC, on an "AI PC." And here they totally missed: a rich advanced user – engineer, IT, or media professional, only cares for how fast his FEM simulation runs, how fast his program compiles, or how fast video gets encoded.

The people who they think are their target market, are the ones needing all those features the least.
 
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