Nvidia RTX Spark

Quite a few of those already have arm ports for windows, and have since the 1st gen Snapdragon X Elite. I have the surface laptop 7 with that chip, and I remember it being made a big deal when photoshop & lightroom were ported. I believe Blender also had an arm build for windows a while ago too as did Davinci Resolve (as of 2024 I believe).

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?

Don't read too much into the marketing speak. "Embracing" does not necessarily mean most of these companies are actually doing anything other than providing a marketing statement about how their product already works on ARM64 Windows. E.g. Photoshop has had an ARM64 version on Windows since 2020, EAC & BattlEye since 2024 & 2025 respectively. And, even then, it'd be a lot more exciting if e.g. Fortnite would actually enable ARM64 support in EAC rather than it just be supported by EAC.

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.

Press releases are easy. Delivering on promises, not so much.

Will this push even more games into Linux?

You know most of them already have arm versions...

So given how ARM has favored closed-ended boxes like the Switch, how is this any different than a Switch with a keyboard and Windows RT?

Somewhere, a monkey’s paw must have curled its finger.

Apple and Steam have been successfully applying pressure for years. Who's willfully staying behind at this point?

For Apple it worked because they waited until they had a really, really good ARM ISA CPU (combined with arguably sandbagging their x86 offering for a few years prior but I digress).

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.

That's surely one thing, Apple went all-in on ARM, for Microsoft it's still a kinda "reduced experience".

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.

I feel like making universal binaries a thing, and pushing for it to be standard is one viable path.

What is GHCP in this context? Glasgow Haskell Compiler Platform? Google Hostage Computer Program?

Can you give some context of how you are getting both of those to work? I am guessing vulkan. Did you face any pain during integration? I am planning to add R9700 to my 5070ti and my only concern is if vulkan wouldn't be able to do the heavy lifiting.

Between that and the Arc Pro B70, they’re the 32gb cards that are actually affordable and worth getting.

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.

I don't think there's any incentive for Nvidia to make this a Windows-only device, so most likely it will be fully supported on Linux, just like their GPUs are.

An nvidia engineer in a discord server said it should work fine

Sort of. It's the same chipset as in the DGX Spark & DGX Station, which run Ubuntu (NVIDIA's flavor).

DGX Spark comes with linux out of the box, it would be hard to imagine this device is not also compatible

Honestly this looks like Microsoft must have thrown a pile of money at them to not mention it, as it's just too obviously the main question.

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.

This is strangely absent from the news.

It's a collaboration with Microsoft so going to say no, probably not.

From what I'm reading it's probably the same chip that's used in the DGX Spark, the memory bandwidth at 300MB/s is equivalent to an M5 Pro, however you can't get an M5 Pro with 128GB of RAM. Apple pushes you to the biggest M5 Max chip, which at the 14 inch form factor, costs you $5099. You can get an ASUS GB10 machine with 2TB storage for $4000, so I guess the RTX Spark laptops will be more than that due to battery and screen, etc.

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?

128 GB memory is also lame. I'm hankering for a windows equivalent of the mac studio that came with 512 GB.

There's a photo here showing 600GBps memory bandwidth so maybe they have doubled it:

https://www.servethehome.com/nvida-introduces-rtx-spark-an-a...

I would love a RTX Spark Shield. ;p

I'm not sure that's such a bad thing. It's not going to challenge the Apple M5, but if you're specifically looking for something in the "not-Mac" market, having a laptop-sized version of the DGX is probably going to be pretty successful.

The main bus is 300gb/sec, which is on par with MB Pro. MB Max has the 600gb/sec of unified memory (about ~500 or so in practice for token generation) only for the 40 core variant, which is like $7k +, which is ironically more expensive than a dual 3090 card desktop. The 32 core variant which is still wildly expensive is like ~400 gb/sec.

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.

Speaking as someone who has had a DGX Spark all year and been active developing at the driver and kernel level for it and other ARM64 Linux devices the last couple of years, it's not bad now and certainly doesn't have any issues that I wouldn't expect to be fully fixed with the second-gen motherboards going into these. The main hardware issues are not with the core SoC. They're replaceable edge peripherals like the PD PMIC.

I cannot think why someone would run those workflows on a Windows laptop, unless someone has way too much money to spend.

Rosetta on Mac was obviously impressive. There was also impressive Arm->Intel translation in the mobile ecosystem at one time.

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.

Apple Silicon has a special mode that modified how the ARM chip handles memory transactions to be like x86. Does this nvidia ARM have the same?

What would be interesting to me would be how quickly developers start targeting ARM64 directly.

DGX Spark is really poor at inference due to the memory bandwidth so hopefully they’ve fixed that before touting this as a way to run local models.

UEFI, display panels, wifi, storage controllers, etc would be what I'm worried about. I doubt Microsoft is going to make it easy.

DGX Spark is also $4700

It is great for inference for single user/single session. it is not replacement for graphical accelerator, that run several concurrent inference sessions in parallel.

Basically the same tradeoff as macmini with unified memory.

The RTX GPU laptops run very hot. Even though they are pound for pound better, it’s just runs too hot for local llm usage for me at least. Prefer Macs for this. A lot of AMD cards also run cooler. I wonder if undervting would help with smaller models and heat.

>That chip isn't great for LLM inference actually.

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.

SuperSpark and then UltraSpark. And then we can get SparkCube, Sparkii, and SparkiiU.

Will we get enterprise ready open firmware too instead of this "we missed DOS so we invented UEFI" for boot firnware?

Local AI was/is bound to happen, eventually. It'd be smart of Nvidia to get ahead of it.

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.

I don't believe Anthropic and OpenAI are any more fearful of local AI than Google or Microsoft are of people hosting their own email.

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.

One can only hope.

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.

It's not even anything new, it's basically the mobile version of the DGX Spark. The two chips (N1X/GB10) are pretty similar in terms of architecture and specs. I don't get why this seems to be getting so much attention now.

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.

You can do a lot with existing devices in a medium to decent gaming PC (or probably phone/laptop, I haven't tried.) I think HN tends to skew toward only thinking of LLM as useful for coding, but they are very useful for many non-coding things, and existing local LLMs are quite capable. I imagine it won't be long before apps with LLM-based features will try to run locally first and fall back to cloud LLMs just to save token costs. Actually I'd be surprised if some apps aren't doing this already.

Might be aimed at people who spec out the $5100 Macbook Pros with M5 Maxes and 128GB.

> 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.

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.

DGX Spark is $4700, so I kind of doubt that RTX Spark's top configs will be cheaper than that.

This feels like getting a foot in the door to ensure Apple doesn't entirely eat Nvidia's lunch if AI inference workloads start to shift from cloud to local.

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.

yes, same chip

+ Windows

+ Screen

- ConnectX-7 Smart NIC

I had a high end spec gaming laptop with 0 airflow back in the 2000s, and used to raid dorm LAN party freezers for frozen meat + towel = heatsink.

I was lucky that iteration 1 (sans towel) didn't ruin the laptop...

No. You can get a PowerBook today with 128 GB ram.

https://www.bhphotovideo.com/c/product/1957120-REG/apple_mbp...

Winvidia

Nvideous

Nvidiows

Nvindows

ARM cpu made by MediaTek.

Spark memory bandwidth is ~300 GB/s. Internal bandwidth is 600 GB/s but that doesn't matter.

128 GB at 600 GB/s for this versus 32 GB at 1800 GB/s for 5090.

This is much better value than 5090, you can run much bigger models.

Yeah and also the quoted 1 PF is only for sparse models (only half that for dense, if that), and the DGX had serious hardware issues: https://x.com/ID_AA_Carmack/status/1982831774850748825

Convince me

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

While I agree with that in principle, it is very worrisome that the prices of personal computers, especially of any personal computer that is not a big desktop, have been increasing continuously.

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".

The best open weight LLMs don't run on this computer, or almost any consumer grade computer. Even the memory requirement for Gemma 4 is out of reach for most consumers (by which I mean those who are not on HN). Unless there is some magic that would make high quality LLMs consume no more than 8GB RAM which makes them usable on a 16GB laptop (which is the norm these days), "local LLM for personal computing" is mostly just a myth.

We're hitting the atomic limits of what's possible with minimum feature size in silicon. It's also very hard to remove 1 kW of heat from a laptop, let alone do it quietly or on battery.

My biggest concern with local LLMs is there just isn't enough RAM or HD space to run multiple models, and the generic LLMs are too generic...

I find it hard to see how that would ever be economical. LLMs need very expensive power hungry chips and datacenters have

- 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.

First we need to actually still be employed, and have them at affordable price.

If we do, it won't be on this chip.

It'll be just another round of the client-side vs server-side processing rounds. We've been through them, we will keep going through them.

i think a lot of that is for government and enterprise use. even for personal computers themselves (i.e.: laptops) they're usually loss leaders, they don't turn profit. You can run a server (and many do) on laptops, but that didn't replace cloud services or server hosting. You can't store enormous amounts of data on your laptop/phone for the llm to use, or access tools the app dev wouldn't want exposed on untrusted devices.

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...

I heard leaked geekbench putting it behind the m3, which is couple years old now.

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.

Thats also possible with usb-c.

"Unified" means that it's shared between CPU and GPU, I believe.

But yes, it tends to be soldered on.

No, but LPDDR means soldered, there are no LPDDR dimms

I think unified RAM means soldered to the SoC, which is in turn soldered to the mainboard

My DGX Sparks are the first and only devices I have with 200W USB-C PD. Low power by AI workstation standards, but intolerable in a laptop.

It's worth noting that Nvidia power management on Linux has been absymal. There also aren't any of the usual power management options to see how much power things are using, which is quite atypical for a modern system.

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...

Mind sharing more details about your use and experience with DGX? I'm just curious

Developers weren’t really “forced” to support ARM. They simply recognized that all future Macs would be ARM, whereas most new PCs would continue to run on x86. So the incentive to adopt ARM was much weaker on the PC side.

They didn’t though. Rosetta 2.

Microsoft can do the same for windows - they need to address the fat bundle solution that Apple came up with, but for Windows, though ..

It’s nivdia attempting to compete with Apple’s M-series

I wouldn't say it's Nvidia stepping back from open source... if anything this is doubling down on it, as one of the selling points of this is the 128GB of unified memory which will allow for hosting local models (i.e, nvidia's new open model they just released). I guess it's pretty cool, I'm a big supporter of local LLMs/open weight models so seems enticing to me, although I'm not sure this will be super applicable to a lot of regular consumers. Seems like a pretty niche product.

Linux works but MS is just paying them not to mention it.

They're Turing complete. What else do you need?

not for llm full training, but can do some finetuning for sure.

I believe training is way more processor intensive than inference.

Have you tried Qwen 3.6 or Gemma 4? They're not frontier level but certainly have their uses.

Strix Halo chips have around 210+ GB/sec gpu memory bandwidth and announcements put the new nvidia chip at around 300GB/sec gpu memory bandwidth.

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.

DGX Spark has the same soc and ships with Ubuntu

Strix halo's 8060S gpu is very weak, and is roughly equivalent to a 4060 laptop GPU, whereas GB10's gpu is equivalent to a desktop 5070. For LLM throughput, tok/s is similar due to bottleneck by memory bandwidth, but the GB10 has 3x faster prefill. People have also been able to squeeze out much better performance on GB10 using NVFP4 and other improvements in the months after the DGX Spark launch, so don't be misled by early lackluster benchmarks. For the RTX Spark, which also targets gaming and creative applications, the 3x faster GPU is quite nice.

Or like a m4 max? This thing has <300GB/s vs the max with 550GB/s

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...

The only Question is is it worth suffering hip and x86? I suspect a lot of folks might like a machine that mimics their GB300 But costs less than a dgx.

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?

Is CUDA really a lead for long? Aren’t all the latest competitive approaches avoiding all the standard software stacks and writing deeply customized software that is very directly tied to whatever hardware they use?

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?

First, make it possible. Then, expand the market. The early adopters help pay R&D for later efforts. Every desk is a good goal, even if not hit by the first doodad.

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.

I don’t know anyone other than a very small but vocal minority who will give a shit about this.

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.

The constant deliberate conflagration of LLMs with general intelligence is so grating.

Don't know about intel, but AMD has Strix Halo with unified memory and really impressive performance.

I think the future will be 50/50 x64 vs arm64 for PCs.

2 comments in total there

Anecdotally Windows ARM works fine for me, although to be honest most of my work is command line + browser anyway. WSL works like a treat. Steam installs and most lower end games also play fine on my ARM laptop too. Games that require kernel anticheat don't work.

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.

The "gaming" take is a strange one indeed for an ARM platform. Hopefully they (Microsoft or Nvidia?) put some real effort into the translation layer. They claim modern AAA games, but it is possible they strongarmed the developers to make them an ARM build for a few select titles...

Who cares about Windows, the goal is to run local AI models similar to AMD Strix Halo and Apple Silicon machines. The OS is honestly a distant last concern as long as the models work well, as you could put Linux on these too, but not sure how well wake lock works.

Hopefully MSFT would look at this as a do or die system, and go all in on improving the user and ownership experience. Will they? Not so sure.

A lot of the app compatibility issues on current machines are down to Qualcomm's poor drivers - the actual core bits are mostly okay.

For reference, this is just a single benchmark, but as an idea of each vendor's top mobile CPU single-threaded performance:

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.

This will likely have worse single threaded performance than recent Qualcomm CPUs.

These chips also appear to be using off-the-shelf ARM cores.

Qualcomm Snapdragon x1 and upcoming x2 use their Oryon core and have much faster single-thread performance than Intel/Amd and this nvidia soc that uses off-the-shelf arm cores

Not at all. This is a more like what Apple has been doing the past few years. A bunch of decent arm cores paired with a beefy integrated GPU.

No.

It won't, the top tier RTX Spark has the same exact CPU and GPU as DGX Spark, so you can check DGX Spark CPU benchmarks to see how it fares. Spoiler: it's about M3 Max level. And they're only coming this fall.

Nah, still ~300GB/s memory bandwidth. That will be slower than the M5 max, by a wide margin for LLM inference.

M5 max is 3x stronger and 50% more power efficient. nice try though.