Nvidia RTX Spark
nvidia.com
nvidia.com
> Over 100 Windows software providers such as Adobe, Blackmagic Design, Blender, CapCut, ComfyUI and OTOY, and game developers such as KRAFTON, NetEase, Remedy Entertainment, Riot Games and XBOX are embracing the new RTX Spark platform. [...] NVIDIA is partnering with Adobe to rearchitect Adobe Premiere and Photoshop for RTX Spark. [0]
> Gaming on Arm is finally coming of age thanks to the NVIDIA partnership. Native anti-cheat solutions from Epic and BattlEye are fully supported on the RTX Spark platform. Major developers are jumping on board, with Riot Games bringing League of Legends and Valorant natively to the architecture, alongside KRAFTON bringing PUBG Battlegrounds. [1]
Also, Nintento Switch is an Nvidia/Arm gaming device so many game publishers already have some experience with the combo.
[0] https://nvidianews.nvidia.com/news/nvidia-microsoft-windows-...
[1] https://www.windowslatest.com/2026/06/01/microsoft-builds-it...
Microsoft pulls in their weight as well, so this seems like it has a decent chance of getting industry support.
Actually, I went to the Mac Studio configure page on Apple.com and you can't do higher than 96GB now...
Vendors didn’t have to do shit to support the platform, they just got better performance if they did (like factorio).
There is something of a difference between “all your stuff will still work, at comparable performance” and arm windows which (as evidenced by all the vendor promises) you can’t really currently say with prism.
I would describe prism as “surprisingly rubbish considering they had an example of how to do it right” and “your app probably doesn’t run because of drivers or some ??? compatibility thing”.
Am I misremembering? I remember being blown away by Rosetta.
Prism… yeah. Toggle the settings. Disable jit. Disable FP. …bin laptop. Get an intel laptop.
For decades, Windows made it too easy for games and even some application to install drivers. Windows games use drivers for anti-cheat (and historically for copy protection too). Neither Apple Rosetta nor Microsoft Prism can translate/emulate drivers, but since drivers have been much more prevalent on Windows, now Windows has a much biggest compatibility problem.
The big news is more so on the games side, which is probably where Nvidia had some pull.
I'm curious what "rearchitect for RTS Spark" means in practice though. Sounds like its less convincing them to make an arm build for windows, but they are maybe taking advantage of some hardware specific features? If so, what does that mean for the Snapdragon X series I wonder?
What would push more games would be Valve actually making it worthwhile to natively target Linux.
So if anything, we need to push more game studios to use open source dependencies which will make porting easier.
Intel has closed things down: some wifi and webcam firmwares are poop and a massive pain to get working on newer chips (if at all). Their wifi firmwares also don't respect certain kernel overrides (which is why I replaced my Intel Wifi 7 chip with a mediatek Wifi 6 one). Blame is 100% on intel and not linux. Broadcom is also pretty bad at being a team player in this regard.
I basically recommend everyone to stick with AMD chipset & GPU's where possible, because they have mainline kernel support nailed down 95% of the time.
Again, ARM works fine, their extra firmwares for extra devices on SoC's are to blame if you struggle.
This isn't fully the reason, Linux is infamous for requiring a specific build for each SoC (and usually each board of said SoC) where as Windows on ARM uses ACPI which Linux doesn't support to the same level. Linux prefers the landfill promoting device tree for each device approach.
no, thats just androids problem. desktop linux like ubuntu ships all the device trees from their supported arm variants. its still more work than acpi for kernel maintainers but you dont need device specific builds.
> landfill promoting device tree for each device
whats landfill promoting about this? you dont need to keep the trees updated they only depend on the hardware and follow a stable spec. and even if the vendors dont want to support linux a community user can write their own device tree and send upstream.
Only the ones which explicitly list something like the Riot Games mention are really related to the device/Nvidia. The thing which really pushes this along is user adoption/market share, not big names. This device will help that, especially in the gaming space, but it's easy to get over eager as it being from Nvidia means everyone else who has been waiting will just now jump on board too because of that.
But I really do question how well Windows on Arm is really going to work out long term.
For Apple it worked because they were able to force the issue. If you wanted a new Mac it was going to be Arm and we all knew eventually (this year or is it next year?) Intel support would drop. Over time we have seen M series exclusive features.
Developers were forced to update or abandon Mac which gave users a great experience (with some early growing pains).
This is something that Windows will never be able too do. They will always be stuck maintaining an emulator and a likely large subset of apps only supporting one over the other. (also does this work the other way around with an Arm only app working on x86?)
This seems like a repeat of when it was not uncommon for games to only support Intel or AMD or NVIDIA or AMD. But worse since they are not both x86. Sure at least we have emulation but just like with Rosetta2 it shouldn't ever be the long term solution.
They only know Apple, Windows and Chromebooks.
Qualcomm is also working on a really good ARM ISA CPU with their acquisition of NuVia and subsequent Oryon architecture.
Meanwhile this is just using off-the-shelf ARM CPUs in a MediaTek SoC with blackwell bolted to the side of it. ARM's CPUs so far have been subpar for laptop-class chips. Hence why neither Apple nor Qualcomm are using them.
tbh, I always read this as Intel doing some sales magic here.
Apple: "Hey, we're making a product that has a 15w thermal envelope, do you have anything?"
Intel: "Yes!"
(Unspoken: their products will throttle down to fit, in fact, they will try to run always at 99ºC so you always get the best performance! FEATURE!)
Apple: "uhhhh..."
Consumers: "HEH IS IT EVEN A PRO DEVICE IF IT DOESN"T HAVE <INTEL MARKETING BRAND TERM>?"
Apple: "UHHHH... Guess we'll do it ourselves"
Possibly, but Apple choosing a new, thicker chassis the same generation that they introduce their more power efficient replacement is certainly a thing. Even if Intel failed to achieve the TDP they told Apple, Apple also seems to no longer believe the thinness they were doing was viable for that TDP anyway.
Intel's product offering certainly wasn't as compelling towards the end there, but it also looked almost uniquely bad in Apple's chassis vs everyone else's
Ultimately, Apple won that fight when they decided to stop letting Intel control their hardware roadmap and it's been a great change for the entire industry. Intel is finally seeing some changes in their own products, largely in response to Apple dropping them. Now Nvidia is getting into the game which means more competition which is also good.
MediaTek is involved in the SoC but both the CPU & GPU from Nvidia are bolted on to it. I.e. it's not a standard MediaTek CPU with an Nvidia GPU added.
But the bigger problem in my opinion: How much of the Windows userbase actually sticks to Windows because of its backwards-compatibility?
--> What would happen if they break this model and the OS is only judged based on its user experience and available applications...?
I'm not sure it would stand any chance to compete in the B2C space. If I think about it, there's not a single new feature in Windows of the last ~20 years I particularly care about.
Without backwards compatibility, there's barely any ecosystem. MacOS on the other hand is full of ecosystem features, improving collaboration, connectivity, handoff across devices, etc.
True, but if you're only in the ecosystem as a mac user, in many ways it's felt like a mixed bag. I still wildly prefer mac over other operating systems, but if upgrades had a price, I think those sales would mostly go to iPhone users. Even at free, I'm yet to find a compelling reason to install Tahoe, and will probably just continue waiting until the next one.
But despite that, as a Windows user I acknowledge that any kind of interaction with another Mac from within MacOS (Handoff, Sidecar, Universal Control, Bluetooth-pairing to Apple-ID instead of Hardware MAC-ID,...) is leaps ahead of what Microsoft was doing with their OS for the past years.
Just the scenario of an employee getting a Windows laptop as a work-PC, there's barely any halo-effect if he/she also uses Windows at home. No easier handoff, no interaction, hardly any "just-works" connectivity.
Windows is mostly a vessel for the (legacy) applications it can run, and for these Browser-based Microsoft Online-Applications (which work equally-well on other platforms)
They didn't invest in creating "just works" frameworks for their PCs which amplify the ecosystem the more compatible devices you have, instead most of their focus is now on "just-works" stuff in the cloud.
So if Microsoft would make a clean cut on backwards-compatibility, I'm not sure there would be a reason left for most B2C users to even stay with Windows.
The "you can make it work if you invest a bit of time or google it" paradigm is nowadays well-covered by Linux already, and it's getting even harder for brands to compete on price/quality with Apple's scale, for almost any portable device...
Recently I upgraded my motherboard and tried reinstalling Win10 Pro, but couldn't activate it despite saving the product key. They have at least THREE obscure flows for re-activation depending on how it was originally activated. The license in my flow needed to have been bound to a Microsoft account that I never previously needed, because it ties itself to the hardware. I had to dismantle and rebuild with my old installation, activate it with my old motherboard on a Microsoft account that I wasn't planning to use to login with, then rebuild again with my new components, sign in to activate, and then disable sign in to be able to use a local user account. Insane.
- 5090/6000 Pro: 1792GB/s
- 5080:: 960GB/s
- 5070Ti: 892GB/s
- M3 Ultra: 819GB/s
- DGX Spark: 273GB/s (less than an M5 Pro at 307GB/s)
Memory bandwidth isn't everything but it will cap inference rate pretty heavily. Also, the M3 Ultra is for an almost 2 year old Mac Studio. It's widely expected that it'll be refreshed in Q3 with a likely M5 or M4 Ultra with >1000GB/s. I really hope Apple realizes what a market opportunity Apple has here.
The above shows just how good value the 5090 really is. It basically a RTX 6000 Pro with less RAM (and ~12% fewer CUDA units), which is a ~$10k card, for 20-30% of the price. This also demonstrates how NVidia uses VRAM for market segmentation. As an aside, the true data center cards (eg B100, H100) use HBM memory at ~3.2TB/s.
[1]: https://wccftech.com/nvidia-enters-pc-space-with-rtx-spark/
This is much better value than 5090, you can run much bigger models.
> tl;dr - For software development, Qwen3.6 27B, 5090 gives you ~3x speed over M5 Max, letting you plow through code, while M5 Max gives you ~4x memory, letting you use higher quantization and bigger context. Which would you choose and why?
I've read a number of things from which the consensus seems to be that yes you can run a larger model and/or have more context with a 128GB+ Mac but the performance gap is still massive and with current hardware we're still talking about inference rates that matter. By this I mean there's a big difference between 10tok/s vs 30. Once we get to t apoint where it's 100 vs 300, it won't be as big of a deal, a bit like FPS in games.
Oh and there are similar concerns with the DGX Spark [2].
[1]: https://www.reddit.com/r/LocalLLaMA/comments/1t5v2gr/need_ad...
[2]: https://www.reddit.com/r/LocalLLaMA/comments/1sqk333/dgx_spa...
The larger memory also allows for pre-training / finetuning models, hence why it's aimed at developers.
I can easily run Qwen3.6 35B-A3B with Q5_K_M with a 260k+ context window with some vram to spare. It easily runs probably 80tps. It took me quite a while to find the
Compared to GHCP Claude Sonnet 4.5 or 4.6, I have full parity. The wall clock time is faster for agentic workflows, and rule following is about on par.
With either, doing something kind of novel or obscure takes more hand holding compared to just generate a GUI or crud app. For example, trying to build an actual program that performs a complicated process correctly requires quite a bit of hand holding to get it to properly help.
Sure, it isn't Opus or something, but I think with the right harness, it probably can get close. I think most of the issues these days is the harnesses are lacking.
I’ve got both (single R9700, dual B70) and they do nicely for about anything I throw at them, such that the latter has a visible improvement when the model is well-cached.
It was suspected to come soon enough, but it was a nice cheap road for my small hobby stuff. When they announced the price changes, I started to explore alternatives, and with the news of Qwen3.6 35B being both and having quality, I figured it was worth a try out, and self-hosting made the most sense to me, since that meant I was free from being a forever-renter.
I'll probably try to figure that problem out in about a month. Worst case is I move it to another even older desktop to replace the 9800 GTX+ inside of that one.
So with proprietary blobs that give you more trouble that they're worth?
Are you refusing to use the Nvidia binary drivers and/or a setup like DKMS that ensures kernel modules are rebuilt as necessary? While I respect the principle, it's a problem you're creating for yourself.
> upgrade my Kernel whenever I want to without getting a black screen with a blinking cursor on the next boot.
Really? I had very much the opposite experience.
But once we're talking about laptops, hybrid graphics etc. it quickly shows that this is not a platform Nvidia cares about.
Just anecdotal, but I never had these issues with the desktop AMD APU I had before it or Intel on board graphics on numerous laptops.
But I would say that as an Ubuntu and Debian user for decades I have no incentive to use anything else on it and I'm just pleased to have a Linux on Aarch64 machine that is well supported for a change.
tbh, I was rather unimpressed with the out-of-box experience for an "ai" computer, you couldn't even run a model locally with the common tools people use (no llama-cpp, ollama, vllm, etc). No huggingface CLI eiher, like come on!
I did put together my eventual setup in a repo https://github.com/verdverm/sparky
I need to update that because I have a nice vllm setup on there now with 4 models running, but should be able to get anyone else going without having to muddle about as I did.
No one seriously cares about this running Windows. We want Steam and CUDA/Ollama, and Windows just gets in the way. nVidia are simply not that oblivious, but I have to admit in their position I'd have considered the Microsoft involvement more trouble than it's worth, which is among the many reasons I'm not a billionaire.
Maybe they think the RAM market is so terrible it will kill the whole initiative regardless.
Has Steam finally started to push for native Linux games instead of translating Windows ones?
The failure of business, only reinforces Windows as the platform most studios reach for.
Buy Windows, buy Visual Studio, pay game engines licenses, let Valve do the work.
This ignoring that current Valve's management doesn't live forever, so who knows what happens afterwards.
Windows' monopoly on game dev isn't just market share either, since game dev isn't just code. You still need Photoshop, Maya, etc. and in smaller studies there's typically a crossover where some devs are doing art as well. Visual Studio's C++ debugger is still one of the best, and the tooling elsewhere hasn't caught up yet (compared to DX + PIX).
Then you also have to solve distribution and handling the fragmented display & audio stack. It's gotten a lot better, but its still a factor.
I'm fine with most of the work going into Wine/Proton. A stable ABI for Linux is a boon, if it happens to be Win32 then so be it.
Valve isn't going to be around forever porting Windows games into Proton, which is actually hardly any different if they would start selling Nintendo games with Dolphin, if we ignore the legal implications for a moment.
Microsoft have spent the whole Nadella era in "oooo cloud" inspired wonder and actively screwed up everything else.
Note how game developers rather spend their working hours in Windows with all its issues, even if they happen to have Linux servers for running their MMOs.
My Steam library is full of old win32 games that run better on Linux than they do on Windows 11. There are some native games appearing because of the Steam Deck, but the fact is they aren't necessary.
Valve aren't simply better at running a platform business, they've thoroughly subverted Microsoft's old one and have done a better job at running that than Microsoft themselves.
Look at the absolute state of the XBox business: all native games, tens of billions spent on something, and yet it's just a trainwreck from top to bottom.
Tens of thousands of Windows games would remain playable with ubiquitous Vulkan-capable hardware and a 500mb Proton runtime?
In truth if AMD or nVidia put their mind to having decent profiling tooling on Linux, and the AI wave suggests they will have no option, then this could readily become a thing.
You’d think in an era where “code is free” there would be an easier story around running local ai than compiling llama.cpp by hand and then spending hours researching flags - only for it to crash from an oom error every ten prompts or so.
Looking at devices like the NVIDIA Shield gives me some hope that NVIDIA will be better than Qualcomm here. I just hope this is not a case where the OEM has to purchase X years of driver support from the chip vendor beforehand, and that NVIDIA will provide support directly itself.
I've heard there's still a large backlog of both software problems, and hardware problems with the platform. The software problems could be fixed with time, but they'll still give a shitty first impression. I'd have thought Nvidia would just bury this and try again with a successor run of silicon with a new design.
This thing seems practically destined to just be a repeat of the Snapdragon laptop debacle.
that's what nvidia is hoping for
DGX Spark runs Linux, and nobody is going to install Windows on that machine. This laptop got it backwards.
If someone decides to run Ollama for local inference with this laptop, they fit perfectly into the "has too much money to waste" bracket, which is addressed by a few other comments in the discussion.
Installing Linux natively on laptops has always had some specific features not working.
Even my Asus netbook, which came with Linux pre-installed, had wlan issues that I learned to work around with, and the driver never supported the same OpenGL version as the Windows one (3.3 vs 4.1).
Linux driver has always been an issue on laptops, but that's not the concern for running Python code.
Perhaps the next generation of the spark will improve on the bandwidth and RAM size numbers. Yes it's a lot like a Strix Halo, but this has CUDA, which will be of interest to developers who want that.
I was looking for AMD AI Max+ 395 laptops recently, and the only ones I've found were 13 inch models, which seems odd from a heat dumping standpoint. I'm looking for 16 inches, I guess the 13 inch form factor would make it easy for commutes where you're taking it to dock to a large monitor at work or home, but no 14 inch screens?
https://www.servethehome.com/nvida-introduces-rtx-spark-an-a...
M5 Max beats it, but for the price of an M5 Max, you are better off just getting a desktop with 2 3090s, which will be cheaper even at current prices.
The biggest thing where this will crush Apple is the initial prefill phase. 6000+ cores vs 32/40, + active cooling with fans. For local llm models, this matters quite a bit more than tokens/second.
In the end, neither are really worth it for llm use compared to just building a desktop and just port forwarding over ssh to ollama.
What would be interesting to me would be how quickly developers start targeting ARM64 directly.
https://docs.nvidia.com/dgx/dgx-spark-porting-guide/porting/...
One reason it works surprisingly well on modern systems is how much is offloaded to the GPU. You aren't going to get great power optimization or anything without it being truly native though.
There are games which are CPU limited though, and it will be interesting how those do. Curiously those also tend to be in engines with Arm support already.
When you lay out the software stack it is essentially OS > Game code > APIs. Both the OS and APIs are native code, it is only that middle point that needs the real work.
This is why x86 to ARM doesn't have such a heavy performance cost. So games can be CPU heavy but if it is heavy at the API end, that isnt a huge issue.
Very cool.
Of course, DGX Spark is a miniPC, so laptops will likely be slower due to power limits/throttling.
https://www.techpowerup.com/gpu-specs/gb10.c4342 https://www.nvidia.com/en-us/products/rtx-spark/
Why do I have the feeling it's been intentionally made to be bad in order to get you on to their most pensive datacenter gear.
At this point, your cost-efficient options include used 3090s, "frankenrigs" using recycled data center cards, and a handful of "workstation" class cards, where the originally high margins and the long enterprise purchasing cycles have kept prices from going up too fast.
In contrast, a lot of these "personal" AI systems are basically a GPU-like core wired to larger amounts of slow RAM. Which is still semi-affordable. Generally speaking, they make for OK chatbots but extremely slow coding agents. Whereas you can run a modestly useful coding agent at reasonable speed on a 3090.
So yeah, a lot of these systems are bit scammy. But not because it's a secret conspiracy to protect data center cards. Rather, there simply isn't enough fast RAM in the entire world. So they'll flog you disappointly slow RAM instead.
TL;dr: Might be useful for some use cases, but benchmark very carefully.
Basically the same tradeoff as macmini with unified memory.
I think its gonna be another failure as we are used to see with the PC market these days.
Around 2-3K USD something with a good GPU + CPU + 128GB of integrated RAM is just going to be an awesome experience.
Considering Mac options are north of 5K+ even on a regular day.
https://www.nvidia.com/content/dam/en-zz/Solutions/networkin....
With MLX, Apple is building an answer to CUDA, and if people start switching from ChatGPT & Claude to some app that runs on their M5, suddenly Apple starts to look like Nvidia's biggest competitor.
If Nvidia doesn't have a pathway towards getting hardware into the hands of consumers, it could be a really difficult road ahead for them.
I'm here for it. Local models can do a lot of what I need at almost no cost, plus the fun of making them work better or building a new system to handle that aspect of my home lab. A Strix Halo system may not be amazingly fast but at 128gb of RAM it can keep up with most open models worth exploring.
Based on June 1 Copilot Pro plan premium token burn and cost, unless you REALLY know how to use cloud AI efficiently and are tooled up to do so a local LLM on hardware you may already own is very appetizing.
I converted a lot of work today to a 6.5gb local LLM on a 12gb GPU and no, it's not as good. But it is 'free' or at least feels that way, especially when I need to redo something and my copilot premium request % doesn't change.
I expect we'll get there in a few years, so perhaps this is Nvidia taking an early step in that direction.
In that case, this goes against Anthropic and OpenAI's business models. Which is a double whammy after Jensen Huang's recent comment about how agentic coding will only increase demand for software engineers, not reduce it.
So it also feels like a part of a budding shift in the competitive tension between the various parts of the AI supply chain.
Non-techy consumers may never do it, but at some point businesses are going to start asking when do they stop paying per token and start running models themselves. Right now the hardware is cost prohibitive, but I doubt that'll always be the case. Eventually the hardware will get cheaper and more available, and Nvidia seems to be betting on that.
They don't care where inference happens, so long as it happens on Nvidia hardware.
And when that happens, the pitch to non-techy users is "Free ChatGPT you can use offline with zero privacy risk". Once hardware accessibility and LLM efficiency advance to the point that this becomes feasible, I suspect it'll result in a much bigger hit to the cloud AI market than many expect.
AIaaS might keep an edge with multi-modal agentic workflows, but for 80% of general use cases, no "secret sauce" needed, the open weight models are already there, and tooling is constantly getting better.
The bottleneck is the cost of local hardware right now.
"Free, private, offline ChatGPT so long as your laptop has X GB of RAM"
Beyond that, I wouldn't underestimate the incentive of "because I can". The "secret sauce" you refer to is effectively just a DB & a while loop that feeds text to a bunch of tensors. If an indie dev decides they want to release something that dismantles the OpenAI & Anthropic moats, there really isn't all that big of a technical barrier stopping them.
This basically creates a bottleneck at the oldest/cheapest Apple Silicon machines, which are already crippled for context prefill.
But honestly, obsoleting a huge number of otherwise great Apple Silicon machines is something Apple would moment consider a major "pro" of building a compelling local AI stack.
With how much speculation around the difficult time Apple has had getting people to upgrade from M1, I'm sure they'd jump at such an opportunity.
- Please buy our new Macbook pro M5 that gives you 20 tokens/s on local 80B LLM
next year - Please buy our new Macbook pro M6 that gives you 25 tokens/s on local 80B LLM
milking product revenue in perpetuity by offering meaningful marginal improvements, while keeping same architecture will be the golden goose for Apple
+plus if it allows to segment market by wallet size into poor/middle/rich classes, thats even better
I think the bigger hang up is that they're still slower and less capable than the frontier models, especially at the hardware specs most home users are likely to have.
the current dielmma for me is how do I install a model on a remote LM Studio device without bypassing Lm Studio to SSH or remote in?
> lms link [servername] get model ?
> lms get [servername] model ?
> lms get model --link [servername] ?
Maybe I need to read the docs again but I swear the only way is remote or go to that device and download via the GUI, ssh in and use the local cli.
Maybe can copy/paste from one device's downloads dir to the server? Maybe I need to try hosting models on my NAS and see if I can download from device 1 then run on device 2 without install/setup?
If the first thing (for example) my mom sees upon installing the app is a dropdown model picker that contains things like "Qwen3.6-35b-a3b-mlx" she will 100% be bouncing off of it.
IMO the best version of this is a custom app/harness with a couple of pre-selected (and ideally fine-tuned) open models that immediately start downloading after checking the system's hardware specs. This would likely be a turn-off to most devs, but is absolutely essential if building an app for general consumers.
After a few generations (and over a decade) that was indistinguishable from the CPU chip itself.
It's a long hyperbole, I know, but I think local inference is inevitable; and the big fishes know it.
Will that be a complex technical setup? An appliance? An additional chip in your motherboard? So transparent it's burned right into the CPU? Those are just implementation details. We're probably just one generational breakthrough away from it.
They will. As some point in the future, people will want everything, they'll prompt full movies because they're bored and want to watch something.
That said, Apple's vertical integration is a massive competitive advantage here, IMO. Nvidia's reliance on Microsoft & Windows for software support likely makes competing w/ Apple an uphill battle.
If/when Local AI gets good enough to compete with Cloud AI on most inference workloads, Apple starts to look like Nvidia's biggest competitor.
While this is admittedly a dream scenario, the biggest downside would be Apple effectively having a monopoly in "Agent-ready" consumer electronics. Hopefully local AI both becomes the norm, and there is sufficient competition among the consumer platforms.
Side-note: I would love to see an "RTX Spark" Framework 13 mainboard at some point.
Apple's vertical integration has led to a Siri overhaul that took half a decade to roll out, and it won't even run locally. They built an NPU coprocessor that's basically dark silicon for expensive inference, and then shipped MLX to stop Tensorflow and Pytorch from replacing Apple's role in the stack entirely. Mac owners are pleading for signed CUDA drivers for the PCIe or Thunderbolt in their $5,000+ Mac Pros. Apple's ecosystem is pure liability for AI, they're not moving any product for datacenter inference and can't even sell the hardware to themselves: https://9to5mac.com/2026/03/02/some-apple-ai-servers-are-rep...
Nvidia's profit margins are safe. Even if the RTX Spark is a completely failed product, Apple is not encroaching on the markets that Nvidia dominates.
In theory, Apple SHOULD have an advantage given they have everything they need in house and can all pull in a unified direction. In practice, it's not always the case that all the teams in a large corporation are all that much better at pulling in the same direction than multiple different corporations in a partnership. And all this will be moot if Local LLMs never catch up to cloud LLMs in terms of quality.
Regardless, it'll be very interesting to see how Nvidia's partnerships with Microsoft & hardware OEMs play out. If the AI inference compute share shifts appreciably to local consumer hardware, I'll want to see strong competition.
Without Khronos involved, I don't think that Apple has the buy-in to create a real industry-scale CUDA alternative. At this point, it might just be most profitable to support CUDA in macOS and give the people what they want.
See a pattern there even the memory companies will probably get designed around the Chinese are in the process of doing it now so will Apple probably for long range survival.
Local AI capabilities are growing at a rapid pace, but so is hosted AI. While you can do a surprising amount of useful work with a model occupying a few to a few hundred gigs of VRAM, the hosted models are going to be way ahead for a long time.
Google/Microsoft and hosting your own email is a byproduct of how difficult (socially, not technically) hosting your own email has become - mostly because SMTP protocol is inherently broken by spam and patched by social construct (trusted nodes, abuse@, 3+ DNS entries and counting, etc). Purely technical solutions, such HashCash etc, got discontinued in exchange for social ones. Central providers made (sometimes in exchange for, sometimes as excuse of, spam protection) self-hosting socially hard.
Now, I wonder if, and how, once Anthropic and OpenAI need to demonstrate profitability, could hamstring local AI. Which has been /so far/ very valuable for me in doing things that hosted providers don't want liability for, and align against (even if totally lawful and fair use!).
- v4.5: 1x cost, 100% quality, 100% speed but maybe sometimes 80% speed because of load - v4.6: 3x cost, 105% quality, 80% speed most of the time depends - v4.7: 9x cost, 115% quality, 90% speed most of the time
Then people will either stick with v4.5 for everything it can do and, if knowledgeable, use v4.7+ for critical or specific tasks.
But if we add the option of:
LocalLLM: one time hardware + electricity cost, good enough quality for 90% of work, good enough speed for 90% of work, no vendor lock in/sudden cost spikes...
Then there is an edge to running it yourself unless you can burn investor cash to get to the next level.
I think the recent headlines on org token spend plus my own experience just today (June 1) with the new Copilot Pro limits is going to push those with the compute to run locally.
As of about 1pm today I did something to hit 47% of my entire June premium requests (copilot Pro, not converted).
As of 2pm I'm using Gemma 4 E4B on a 12gb GPU (with large context window) off my desktop to power VS Code with Copilot on my laptop. I'm going to build an AMD Strix Halo system next week when parts arrive so I can queue up a few models in parallel or work with something I need that much RAM for.
I'm not lifting the earth with my LLM setup. Gemma 4 E4B is solid for accelerating my current projects. and it's costing me pennies more per hour vs blowing half my Copilot Pro plan in a distracted morning.
I'm at a vendor conference this weekend that is showing off their Agent/Agentic workflows. Nobody can tell me how they balance the cost long term. Hopefully whoever the vendor is paying for their cloud LLM token usage doesn't spike cost in a year (or the vendor themselves) after companies convert and are trapped VMware style with these agent processes. You can bring your own (cloud) model subscription. I need to find out if we can point it back to our own local LLM endpoint and try local models for the same processes. Even if it takes 5x longer, it could be cheaper and more secure.
AI inference is different. You get the outcome by passing text through some weights at the time you need it. There's no ongoing work besides training and releasing new models. If I had something that rivalled Opus 4+ I could use locally, I would switch in a heartbeat.
The writing is on the wall, neither Anthropic nor OpenAI are anywhere near close to sustainability and if one or, worse, both fail the entire demand bubble for NVDA crashes.
It's smart to set up alternative destination markets while they can do so in peace.
But I like it. It's a copy of Apple's SoC design philosophy, same as AMD's Strix Halo, which I always thought was really cool both for laptops and home PCs. NVidia's traditional consumer cards pull way too much power and are too noisy to comfortably put them in a living or office environment.
+ Windows
+ Screen
- ConnectX-7 Smart NIC
Physically, NVIDIA did the GPU chiplet and Mediatek did the other chiplet that has the CPU, DRAM controller, and IO.
Well, MediaTek actually said they made most of the SoC in fact. But the actual CPU cores themselves are all but certainly off-the-shelf Cortex parts, since MediaTek doesn't have a custom core design at all afaik.
Can the link type be toggled between Ethernet and Infiniband? (Don't think I've ever heard of a laptop with IB.)
https://www.bhphotovideo.com/c/product/1957120-REG/apple_mbp...
$3649 with 128GB of ram
Bosgame M5 AI Mini Desktop Ryzen AI Max+ 395 96GB variant €1.800,95 (sold out)
128GB+2TB variant €2.401,95 (in stock)
I have the latter, it's fantastic
It's just worse Strix Halo, as you are landing square in middle of Windows ARM problems
I 'd say that is an improvement if you want to run local llm inference. Still well below with what you can achieve with Apple chips though.
> "Our goal is to deliver unmetered intelligence to every home and every desk with Windows," said Satya Nadella, chairman and head of Microsoft.
Then:
> However, Ian Fogg, Research Director at industry analyst firm FDM CCS Insight said the change was "likely to come with a significant price tag" and Nvidia would be targeting "those looking for workstation-class performance".
So... not every desk with Windows.
It just feels too much like what they said about Apple II and early Windows. A play at nostalgia instead putting real thought into it.
My question is, what happens to the people who use RTX cards for gaming? This new solution isn't meant for that. Do they need an "AI accelerator" and a gaming-centric GPU?
Even in the analytics side most of the stuff is some shonky ass numpy or excel gank.
I don’t know what the market is. I just can’t see it.
A powerful new chapter for Windows PCs, accelerated by Nvidia RTX Spark
https://news.ycombinator.com/item?id=48352693
Surface Laptop Ultra: Made for World Makers
bechmarks with DGX arnt spectacular for NVIDIAs software and CUDA lead.
wouldnt count on this being a price/compute challenger. especially with overpriced VRAM.
All those CUDA cores in the sparks but they're starved for memory bandwidth.
I am still waiting for NVidia to release a system that legit beats 3090 maxxing for the home gamer...
Spark:
OS: Windows/Ubuntu
Mbw: 300GB/s
Cuda cores: 6000
GPU accelerated containers: yes
M5 max:
OS: macOS
Mbw: 600GB/s
Cuda cores: 0
GPU accelerated containers: noThe sparks are good if your ultimate plan is to spend even more on NVidia hardware in future to run your dev setups at usable speeds. Or, you're developing for a work cluster.
If you mainly want to run local models at acceptable speeds portably, buy a mac with lots of RAM. If you’re happy with non-portable / racked, buy 3090s (dense) or mac studios (MoEs). Buy newer cards if you are restricted on power or slots. If you are rich, buy a6000 blackwells.
And is it really a way to lock in people? With AI coding tools, isn’t it trivial to write software on top of CUDA and rewrite it to target some other hardware?
no.
Also I heard the tensor core instructions on the dgx are gimped and you’re better off with a rtx pro x000. Is that the same with these machines?
Eventually a lot of inference will get right-sized into something you affordably run yourself.
NVIDIA and Microsoft Reinvent Windows PCs for the Age of Personal AI
https://news.ycombinator.com/item?id=48352705
NVIDIA DGX Station for Windows Puts a Trillion-Parameter AI Supercomputer on Every Enterprise Desk
https://news.ycombinator.com/item?id=48352691
Introducing Surface Laptop Ultra: Made for world makers
https://news.ycombinator.com/item?id=48352627
Introducing a powerful new chapter for Windows PCs, accelerated by NVIDIA RTX Spark
Sure the graphics capabilities are probably very good. But if you’re a game developer who has traditionally built on Windows on x86 chips, would you want to invest in this new chip or invest in making games for the Apple ecosystem? Aren’t there more new customers to reach in the Apple world than this new Nvidia world?
Windows and the new chip. Higher developer productivity and higher chances of a substantial audience.
I think they make a great "second device" where you have something meatier to fall back to if something doesn't quite work right. I'm not sure if it's ready to take on the "main device" role just yet. But it's a far far better experience than the Surface RT days.
In university a friend of mine had a large hardcover book she kept in her dorm freezer. I asked here WTF she had a big book in there. She said it was for minecraft - she'd place her laptop on top of it while playing. The book was cold but also quite dry. I wonder how well it worked.
I was lucky that iteration 1 (sans towel) didn't ruin the laptop...
1. in order to run LLMs, especially the best ones, you need complicated devices which are expensive
2. if you buy one for your personal use, you are probably not going to utilize it all the time and it will be idle a lot
It seems to me that it will always be more economical that the LLM-running devices are in a datacenter where it is easier to make sure they are always utilized
Local models today are fine for a lot of mundane tasks and will continue to be so. The use cases where paying for frontier models is worth it, will continue to shrink for folks not doing frontier work.
Or stall. Acceleration has been slowing significantly and gains seem to be tied to huge memory footprints.
Think of it like having a graphics card at home versus using a cloud gaming stream? Technically subscribing to GeForce is much cheaper up front than getting a card, but people still do that. So will the audience of people running agents at home be as large as PC gaming? I think that's kind of plausible.
That is not how LLMs are typically used though in my experience
> Think of it like having a graphics card at home versus using a cloud gaming stream?
Latency seems to be much more important in that use case
2. Eventually we'll get to where local models that don't have sycophancy and slot-machine mechanics trained into them will perform better.
I think consumers are primed for that type of behaviour though. I have an iPhone on my desk. It has something like 2-3tflops CPU+GPU, which is double that of the largest super computer on earth when Jurassic Park came out, and is probably more computing power than existed on earth when I was born in the 80s.
I use this device for around 1hr per day to write text messages.
There is no ceiling to the power of consumer hardware. If it's cheap enough, it will be bought.
SETI@Home is a very niche use case
and web browsing still happens by connecting to data centers and server farms, not by connecting to another laptop
Even two or three years people were pointing out "The ChatGPT subscriptions you can buy with $2000 give you much more compute than whatever home setup you come up with" on r/LocalLLM. I did my own elementary school maths and came to the same conclusion.
Yet till this day people still boast how their beefy M4 Pro/Max machine with 32+GB RAM (which is not at all a "normal person's setup" and costs $2000+) runs LLMs smoothly, and "that's the future".
Someone needs to re-learn basic maths and take a walk around Best Buy to understand what "consumer laptop" looks like.
AI vendors are really going to struggle to shift tokens far beyond the frontier of human capabilities. It's reasonable (not guaranteed) to assume that, if the trend of frontier models (doubling capabilities on benchmarks every n months) holds, then the same trend will hold for local models, and those local models will meet and exceed the perception frontier. This would mean a human cannot tell the difference between Mistral-Open-2030 and Claude Opus 2030.
That's a bunch of "ifs", but there's nothing exceptional about those "ifs". They're basically the scenario if nothing changes between now and ~2030 with regards to capabilities trend attainment.
The whole replacing people angle is just the short term use case the more ghoulish executives are thinking about. In practice, lots of lots of new use cases have been made possible by LLMs. A lot of which can be done locally. But whatever capacity you have locally, they can have more of and for cheaper, and they manage the model instead of you doing it yourself. I think you put it nicely though, their moat will be thinned, and I doubt they'll be as profitable as their funding suggests, but at the same time the demand for them won't go away either. I don't know if OpenAI and Anthropic will be viable, but I'm nearly certain Deepseek is.
The tipping point will be power usage, if a local llm can run the same workload for less power that would be a game changer. Nvidia might get decimated, but even Google and others have moved on from GPUs already, they have faster and more power efficient TPUs. Add to that network bandwidth and availability issues, their moat remains. Also consider that even for graphics capabilities, user devices just don't have a consistent spec to make things like widespread 3d graphics and webgl usage viable. Someone's cheap android phone will never run a local llm reliably,same as it won't a 3d game. even if they have a high-end iphone, network providers aren't always performant as they are in western countries, and then there are people that won't want to install your app or local software, and then browser based exposure of the capability to sites which will have similar hardware spec issues, OS instabilities, competing tabs,etc...
- bulk discounts - cheaper electricity - high utilisation to spread the costs among many users
I don't see how PCs could ever compete against it. Most users AI demands would probably result in >90% idle time on the GPU.
The price of a mini-PC with Intel Panther Lake is at least double in comparison with the price of a mini-PC with Arrow Lake H having similar specifications, and I am talking about barebones, before adding DRAM and SSDs, whose prices have risen even more.
The rise in prices is somewhat obfuscated by the confusing names of CPUs, i.e. some old and new CPUs may seem to be at similar prices and they have similar names, but the new CPU actually corresponds to a lower segment of the market, by having e.g. a smaller GPU and a lower clock frequency, while the CPU model that really corresponds to the old is named such that it seems to belong to the class corresponding to its present price.
As a concrete example of this obfuscation, which may confuse the buyers of laptops or mini-PCs, I have an ASUS 15 Pro with "Core Ultra 5 225H". If I would buy an ASUS 16 Pro now, the corresponding CPU model, the cheapest which is not worse than what I have, would be "Core Ultra X7 358H".
All I care about is if I can get one of these for significantly less than a dgx and get Linux on it for some cuda Blackwell kerneling.
knowing nvidia and their approach to consumer hardware (especially those on notebooks), i'd take a heavy grain of salt on getting a good deal.
also, they've been bad to their ecosystem partners, but now they've been able to boss them around into supporting them so far. i suppose getting anticheat vendors to finally support arm might be enough of a nudge for linux support? one can only dream
Maybe the Nth time's the charm and Microsoft+Nvidia will manage to make Windows on ARM a viable platform.
Saying that I think this is product is kinda dead on arrival.
What does AMD or Intel have here?
I think the future will be 50/50 x64 vs arm64 for PCs.
I'm not sure if I like this. Sure for a laptop this might be not a big problem but if this ARM ecosystem is a success it will spread to desktop computers and I fear we could lose the existing modularity.
But yes, it tends to be soldered on.
Geekbench cpu bench leaks indicate they aren’t as good as m3 at single core even.
Will they support booting into a Linux installer?
I think more announcements will follow soon from other companies.
Nvidia really threw stuff over the wall with the DGX Spark release. They don't seem to really care. I sort of think they'll spend a little more time on Windows, where there's no pesky upstreaming to do and they can just do whatever, but man, it's such typical hubris from Nvidia to build such an expensive box with good chips but make it basically unsupportable and roasty hot all the time.
You also generally have to run an ever more stale two year old Ubuntu derived DGX OS to get anywhere, with bespoke kernel and drivers all. None of it is well supported, none of it just works like a comparable PC or even well behaved arm system would.
As for other ARM, there were rumors AMD Sound Wave is/was going to be a ~10W arm APU, but there hasn't been much said about it lately. Honestly given the ram crunch, it's maybe just not worth trying to build a system with a cheap core, if the rest of your costs are going to stay so stratospheric. https://www.techpowerup.com/341848/amd-sound-wave-arm-powere...
Although I'm kinda surprised the DGX Spark used USB-C at all for power instead of just like a DC jack or whatever. But whatever.
One might call this "forcing"
"Introducing the NVIDIA RTX Spark™ Superchip. The fusion of NVIDIA AI and RTX graphics in a single chip redefines Windows PCs and delivers amazing creating, AI development, and gaming—on the slimmest, most beautiful RTX laptops ever and small, ultra-efficient desktops."
Nvidia is also very very rich and pushes the boundaries of stuff. They stoped waiting for industry standards. You can see this in there network stuff. All nvidia.
Next logical step (at least now, not something i thought about) was there CPU for their GPU racks/clusters/systems.
Now they have everything anyway, RTX Spark is just logical.
I don't think its specificly targeted at Apple at all.
Apple has like 10-15% market share and just because some IT nerds buy themselves a mac mini doesn't mean much.
Plenty of them actually just run openclaw without local models. Something which surprised me quite a lot.
But i have two 4090 at home. They consume a lot of power and i had to research the proper Mainboardmodel and had to mod one 4090 to use water cooling because they run too hot.
There Spark setup was at 3k, way to expensive for normal people. If they can get this down and sell more, great for their ecosystem (strengthening it) and getting more money from people.
It does surprise me though that they have enough capacity for this chip and not just putting everyting in Rubin but perhaps the build out has slowed down a little or they start to diverse already for economic savety
Guess I need to postpone my gamer PC renewal to end 2030.
NVIDIA nailed it
I don't think so.
This most likely be a winmodem situation, again
More seriously, obviously a ton of work in an incredibly competitive space, and an incredible machine (without getting into competitive comparisons/minutiae). Was watching a techtechpotato[0] quick post pre-launch about "why is this even being tried?", which was also interesting. What an age we live in.
However, I'd jump from Mac in a Heartbeat if this supported Linux.
I was disappointed to see that the RTX Spark has the ARM cores from the DGX Spark. I was hoping it had their new in-house developed cores that Nvidia is starting to use on their latest gen server parts. They look really fast. That said, if RTX Spark has CPU performance like the DGX Spark, it will be almost as fast as the top AMD/Intel parts.
Geekbench Single Thread Score:
- DGX Spark (same CPU as RTX Spark): 3125
- Snapdragon X1 Elite: 2950
- Snapdragon X2 Elite Extreme: 4050
- AMD Ryzen 9 9955HX: 3225
- Intel Core Ultra 9 290HX Plus: 3175
- Apple M5 Max: 4350
I'm happy to be wrong about Qualcomm's latest X2 chip performance, even if it is shipping in only a single product so far. Their previous best was the lowest in this list.
https://www.gartner.com/en/newsroom/press-releases/2026-4-10...