Wtf. Are we actually seeing a push towards being 100percent dependent on the cloud?
Sovereign AI has never been more important.
Controlling AI is so much more powerful and impactful than controlling search or social algorithms.
Centralized AI a 10x on social control over everything we have seen before, and there is no way that a free and open society survives contact with centralised, high capability AI if the system is manipulated towards guiding outcomes. It’s essentially mind control, as people will offload an even greater share of their cognition to AI than they do with search and social.
Build and maintain sovereign AI while you still can.
No, we are just seeing a push towards Nvidia wanting to make money.
Nothing stops you from giving Nvidia half a million for their latest and greatest AI GPUs, unless you are in China I guess, but China does very well despite the export control.
It is just classic market segmentation, the thing Nvidia and just about every company that can benefit from it has done for decades. Cloud providers are ready to pay a lot of money, consumers wanting local AI have a lower budget, and gamers even lower. But a low budget is greater than zero, and there is still money to be made on these markets. But they also don't want to compete against themselves, so they are going to find ways of crippling enterprise-class workloads on consumer hardware.
It used to be that they crippled high precision arithmetic on consumer hardware, because it was a differentiating factor, but AI doesn't care about high precision, so now, it is something else.
The reason why open source couldn't win search and social was because they need network effects which is not possible for open source maintainers.
AI is a software just like operating systems and executables where open source absolutely dominates the industry. Remember when the compiler engineers optimized the shit out of llama.cpp.
Right now the bottleneck for AI software is compute and data. Moore's law is give us compute, StackOverflow, arXiv (and it's children like bioRxiv), gutenberg and others (3lib, anna, internet.org) will give us data.
NVIDIA probably already figured out that local will be massive part and are preparing to do DRM for AI.
Pay up for license to unlock full capacity, pay up for license to use encrypted models from AI moguls, paid per billion tokens processed or just operations in GPU
I don't see the case for DRM protected local models on either side.
The consumer has access to free and open models for local use.
The supplier has better control and wider market to serve running the models in data centers.
Only way I can see encrypted models being widespread is if they are bundled as part of a software package (OS/Browser/etc) and not sold as the product.
Years ago I would think I was being paranoid to think they would patch hardware retroactively to make it slower so you need to pay more to use your own hardware.
In 2026, I think this is what all car companies are doing after you pay the full car price.
I don't see a reason why they wouldn't do it with so little competition
Yes? How is this at all surprising given....everything we've seen tech companies push for for years and years? People are fully embedding themselves and their workflows in the services of big, closed companies, and are going to be surprised when they finally feel able to clamp down? There has been warning sign after warning sign, but people are only increasing their dependence.
Nvidia doing what Nvidia has always done; proprietary closed drivers. Weird choice to cryptographically protect the power settings. Key problem is that we've allowed a company that's always been this way to form a monopoly. Other brands exist, perhaps it is now worth the effort to migrate and rebuild elsewhere.
Enumerating 3 things really ..sparks a comment nowadays? Doing that as a rhetorical device has been a thing for generations and of course LLMs mimic it because it is everywhere in their training data.
I just heard a quick J. Talarico speech at some country concert. In the 10 or so sentences he spoke, he had 3 or 4 instances of doing this "Claudism" enumeration. Are you saying Texas just elected Claude for Senate?
Maybe we should stop with such superficial judgements. It's the modern equivalent of not liking the speaker's tie. (Doesn't mean the speaker's speech is good, but it doesn't mean it's bad either.)
Fortunately that's just a matter of weeks until someone revengs and publishes the fix... even if we need to wait a while for a proper upstreamed solution.
As someone who went down the rabbit hole of local AI, there is so much exhausting debugging already. Even if this is "just" another step you have to find, it's toxic.
Why does the title say DGX spark when the link is the RTX Spark laptop?
Those are very different things as far as I understand, and the whole "doesn't work well with linux" really only makes sense on the RTX Spark laptop I think?
Isn't the DGX spark linux by default?
Not everyone grew up in a situation where home was a stable dependable place. Not everyone has a home to go to. If you have it with you, they can't mess with it or take it away from you when you're not there. If you grew up in a broken home, you would understand.
I have been saying this for years, and the answer hits a nerve that gets you downvoted into oblivion: people prefer the aesthetics of laptops, simple as that. They will absolutely go to the grave arguing that they need the mobility etc, then just leave it plugged in. They simply prefer how laptops look, and they absolutely hate it when someone dares say it.
I have a laptop for work. 95% of the time i'd prefer a tower or at least a mini PC with proper cooling, but that remaining 5% i have to be around with the laptop is enough. I don't like the idea of having to remember to sync the projects every time (especially while i'm in the middle of testing something new so it's not like i can just pull the latest commit or sync the latest good version of something)
> why anyone would want to do inference on a laptop
Because reviewers tell them this is is a good use case, and people want to take advantage of the HW they buy [0]. Unfortunately those reviewers didn't take any extra step to validate anything beyond the default config, out of the box.
It should be possible to reverse engineer this, if it works on windows?
> The laptops also support unified memory configurations up to 128GB, shared between CPU and GPU, which is unusual for a laptop and central to why they’re being marketed at AI developers in the first place.
This isn't rare on laptops, pretty default on AMD with iGPU
Unrelated to this slowdown, I could imagine this sort of thing being the right application for things like Taalas, although I'm not quite sure whether it can actually offer what I imagine that it can (i.e. ability to make a cheap, physically small card that can run a large model).
A laptop can't run the latest big models, because memory is too expensive. This thing probably can't run models larger than Mistral Small 4. But it'd be a completely different thing to have a hardcoded Mistral Large 4, than a choice of tiny models.
You probably won't be able to run a sota model on a single Talaas card. You probably need a set. It should theoretically be cheaper, faster, more power efficient than Nvidia at running that model. But then you are stuck with a chip that can run only that model. And I understand that context size will also be a problem.
Yeah, that was why I felt that, while Taalas might not be the right thing in general, it could be really appealing in a laptop, that would otherwise not be able to run any big model at all.
I think I misunderstood what the technology could do a little bit, because I imagined these fixed matrix multiplies encoded into hardware as small-ish, as using routing space etc., not just fast.
I think what I really imagined was useful would be more easily achieved by having a big ROM with model weights connected to an ordinary GPU.
This one is allegedly/hopefully a bug but just waiting for it to become a revenue stream / paywall. Uh oh, you want to run locally your dangerous illegal foreign open models, not licensed by Nvidia? Can’t work with you paying for a special security feature unlock on your GPU.
Or maybe just not at all - shell out for OAI tokens and send us all your data - your privacy is very valuable to us and we will make sure to be the only ones to monetize it.
You could say, Nvidia is selling pickaxes and has no business gatekeeping what rocks you hit with it, but I guess it depends on their relationships with major mining companies, and ultimately what pays more.
The attack on reverse engineering of games is a precursor to universal computing lockdown. When you need AI to keep up with programming, the entity controlling the AI controls what software is possible to create. It’s a bid for centralised control and thought censorship at a scale and depth much more ambitious than anything we have ever seen before.
I'm starting to feel empathy with those people who think bus lanes are coming form their freedom as part of some conspiracy to take away their cars.. I would like private enjoyment of my own RAM to use as I wish please.
Sovereign AI has never been more important.
Controlling AI is so much more powerful and impactful than controlling search or social algorithms.
Centralized AI a 10x on social control over everything we have seen before, and there is no way that a free and open society survives contact with centralised, high capability AI if the system is manipulated towards guiding outcomes. It’s essentially mind control, as people will offload an even greater share of their cognition to AI than they do with search and social.
Build and maintain sovereign AI while you still can.
Nothing stops you from giving Nvidia half a million for their latest and greatest AI GPUs, unless you are in China I guess, but China does very well despite the export control.
It is just classic market segmentation, the thing Nvidia and just about every company that can benefit from it has done for decades. Cloud providers are ready to pay a lot of money, consumers wanting local AI have a lower budget, and gamers even lower. But a low budget is greater than zero, and there is still money to be made on these markets. But they also don't want to compete against themselves, so they are going to find ways of crippling enterprise-class workloads on consumer hardware.
It used to be that they crippled high precision arithmetic on consumer hardware, because it was a differentiating factor, but AI doesn't care about high precision, so now, it is something else.
AI is a software just like operating systems and executables where open source absolutely dominates the industry. Remember when the compiler engineers optimized the shit out of llama.cpp.
Right now the bottleneck for AI software is compute and data. Moore's law is give us compute, StackOverflow, arXiv (and it's children like bioRxiv), gutenberg and others (3lib, anna, internet.org) will give us data.
It's a waiting game. Open source always wins.
Pay up for license to unlock full capacity, pay up for license to use encrypted models from AI moguls, paid per billion tokens processed or just operations in GPU
The consumer has access to free and open models for local use.
The supplier has better control and wider market to serve running the models in data centers.
Only way I can see encrypted models being widespread is if they are bundled as part of a software package (OS/Browser/etc) and not sold as the product.
Man it really is exhausting to read a title that sparks my interest, just to be greeted with the most basic Claude-isms in the subheading.
I just heard a quick J. Talarico speech at some country concert. In the 10 or so sentences he spoke, he had 3 or 4 instances of doing this "Claudism" enumeration. Are you saying Texas just elected Claude for Senate?
Maybe we should stop with such superficial judgements. It's the modern equivalent of not liking the speaker's tie. (Doesn't mean the speaker's speech is good, but it doesn't mean it's bad either.)
I see what you did there. ;)
Those are very different things as far as I understand, and the whole "doesn't work well with linux" really only makes sense on the RTX Spark laptop I think? Isn't the DGX spark linux by default?
If you really want to do DIY inference, plug it in at home and set up tailscale so you can access it on the go.
No huge battery drain or toasty hot laptop required. Lower cost laptop. Lighter laptop. Longer battery life laptop. Win win win win.
unique local data and know how not shared with cloud.
Because reviewers tell them this is is a good use case, and people want to take advantage of the HW they buy [0]. Unfortunately those reviewers didn't take any extra step to validate anything beyond the default config, out of the box.
[0] https://arstechnica.com/reviews/2026/10/the-surface-laptop-u...
> The laptops also support unified memory configurations up to 128GB, shared between CPU and GPU, which is unusual for a laptop and central to why they’re being marketed at AI developers in the first place.
This isn't rare on laptops, pretty default on AMD with iGPU
A laptop can't run the latest big models, because memory is too expensive. This thing probably can't run models larger than Mistral Small 4. But it'd be a completely different thing to have a hardcoded Mistral Large 4, than a choice of tiny models.
I think I misunderstood what the technology could do a little bit, because I imagined these fixed matrix multiplies encoded into hardware as small-ish, as using routing space etc., not just fast.
I think what I really imagined was useful would be more easily achieved by having a big ROM with model weights connected to an ordinary GPU.
But with Nvidia people just expect they nerf all the features.
Or maybe just not at all - shell out for OAI tokens and send us all your data - your privacy is very valuable to us and we will make sure to be the only ones to monetize it.
You could say, Nvidia is selling pickaxes and has no business gatekeeping what rocks you hit with it, but I guess it depends on their relationships with major mining companies, and ultimately what pays more.
But, on the other hand, having my DGX Spark stack at hand, I know the solution:
"AI views proprietary components as damage and routes around them."Sovereign AI has never been more important.