Nvidia RTX Spark encrypted firmware slows inference speed to 35% on Linux

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.

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.

I remain puzzled why anyone would want to do inference on a laptop if they have a home internet connection and a phone.

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.

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.

> A hands-on look at dual-booting Linux on the Nvidia RTX Spark laptop, the GPU clock throttling bug found, and why WSL still wins.

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.

The article is about the RTX Spark, not the DGX Spark. The title should be changed.

Remember when Apple was slowing down iPhones, the outrage!

But with Nvidia people just expect they nerf all the features.

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?

Have you tried lact to increase clock/power level? https://flathub.org/en/apps/io.github.ilya_zlobintsev.LACT

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.

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

Decompile time

As a DGX Spark user, this article title gave me conniptions.

But, on the other hand, having my DGX Spark stack at hand, I know the solution:

    /btw reverse engineer the Nvidia Platform Controllers and Framework component and build a Linux version

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.

Wtf is Nvidia doing?! I was considering buying one, but now?

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