From the FAQ… doesn’t seem promising when they ask and then evade a crucial question.
> What is the memory bandwidth supported by Ascent GX10? AI applications often require a bigger memory. With the NVIDIA Blackwell GPU that supports 128GB of unified memory, ASUS Ascent GX10 is an AI supercomputer that enables faster training, better real-time inference, and support larger models like LLMs.
For comparison, the RTX 5090 has a memory bandwidth of 1,792 GB/s. The GX10 will likely be quite disappointing in terms of tokens per second and therefore not well suited for real-time interaction with a state-of-the-art large language model or as a coding assistant.
This is a tangent, but the little pop up example for their ai chat bot to try and entice me to use it was something along the lines of “what are the specs?”
How great would it be if instead of shoving these bots to help decipher the marketing speak they just had the specs right up front?
Seems this is basically DGX Spark with 1TB of disk so about $1000 bucks cheaper. DGX Spark has not been received well (at least online, Carmack saying it runs at half the spec, low memory bandwidth etc.) so perhaps this is way to reduce buyers regret, you are out only $3000 and not $4000 (with DGX Spark).
Some of the stuff in the Carmack thread made it sound like it could be due to thermals, so maybe could reach or come a lot closer to, but not sustain, and if this has better cooling maybe it does better? I might be off on that.
I ordered one that arrived last week. It seems like a great idea with horrible execution. The UI shows strange glitchy/artifacts occasionally as if there's a hardware failure.
Regarding limited memory bandwidth: my impression is that this is part of the onramp for the DGX Cloud. Heavy lifting/production workloads will still need to be run in the cloud.
I wonder why they even added this to the FAQ if they're gonna weasel their way around it and not answer properly?
> What is the memory bandwidth supported by Ascent GX10?
> AI applications often require a bigger memory. With the NVIDIA Blackwell GPU that supports 128GB of unified memory, ASUS Ascent GX10 is an AI supercomputer that enables faster training, better real-time inference, and support larger models like LLMs.
Never seen anything like that before. I wonder if this product page is actually done and was ready to be public?
Taiwanese companies are legendary for producing baller hardware with terrible marketing and documentation that answers important questions. It's like those teams don't talk to each other inside the business.
Fortunately, their products are also easy to crack open and probe.
It seamlessly combines Nvidia's price gouging and ASUS's shady tactics. God forbid you ever have to RMA it, they'll probably brake it and blame it on you.
How is it different from their consumer GPU marketing? They have Founder Edition under NVIDIA brand initially, but the ecosystem is supposed to mass produce. It appears to be the same for DGX Spark where PNY has produced the NVIDIA branded and now you're going to see ASUS and Dell and others make similar PCs under their brand.
As far as I can tell these are all the same hardware just different enclosures. I'm not sure why Nvidia went this route given that they have a first party device. Usually you only see this when the original manufacturer doesn't want to be in the distribution or support game.
If this is anything like their consumer graphics cards, the first-party version will only be available in the dozen or so countries where Nvidia has established direct distribution channels and they'll defer to the third-parties everywhere else.
Is there something similar with twice the memory/bandwidth? That's a use case that I would seriously consider to run any frontier open source model locally, at usable speed. 128GB is almost enough.
It's just Ubuntu with precanned Nvidia software, otherwise it's a "normal" UEFI + ACPI booting machine, just like any x86 desktop. People have already installed NixOS and Fedora 43, and you can even go ahead and then install CUDA and it will work, too. (You might be able to forgo the nvidia modules and run upstream Mesa+NVK, even.) It's very different from Jetson and much more like a normal x86 desktop.
The kernel is patched (and maintained by Canonical, not Nvidia) but the patches hanging off their 6.17-next branch didn't look outrageous to me. The main hitch right now is that upstream doesn't have a Realtek r8127 driver for the ethernet controller. There were also some mediatek-related patches that were probably relevant as they designed the CPU die.
Overall it feels close to full upstream support (to be clear: you CAN boot this system with a fully upstream kernel, today). And booting with UEFI means you can just use the nvidia patches on $YOUR_FAVORITE_DISTRO and reboot, no need to fiddle with or inject the proper device trees or whatever.
I assume the driver code just isn't in mainline linux and installing the correct toolchain isn't always easy. Having it turnkey available is nice and fundamentally new hardware just isn't going to have day 1 linux support.
You're free to lift the kernel and any drivers/libraries and run them on your distribution of choice, it'll just be hacky.
The resale cost shouldn't be ignored either, that Mac Studio will definitely resell for more than this will by a significant amount. Least of all because the Mac Studio is useful in all kinds of industries whereas this is quite niche.
CUDA is only on nvidia GPUs, I guess a RTX Pro 6000 would get you close, two of them are 192GB in total. Vastly increased memory bandwidth too. Maybe two/four of the older A100/A6000 could do the trick too.
My reasons for not choosing an Apple product for such a use-case:
1- I vote with my wallet, do I want to pay a company to be my digital overlord, doing everything they can to keep me inside their ecosystem? I put too much effort to earn my freedom to give it up that easily.
2- Software: Almost certainly, I would want to run linux on this. Do I want to have something that has or eventually will have great mainstream linux support, or something with closed specs that people in Asahi try to support with incredible skills and effort? I prefer the system with openly available specs.
I've extensively used mac, iphone, ipad over time. The only apple device I ever bought was an ipad, and I would never buy it, if I knew they deliberately disable multitasking on it.
For how shit it all is, it's still the easiest to use, with most available resources when you inevitable need to dig through stuff. Just things like nsight GUI and available debugging options ends up bringing together a better developer experience compared to other ecosystems. I do hope the competitors get better though because the current de facto monopoly helps no-one.
On the networking side. M4 max does have thunderbolt 5, 80gbps advertised.
Would ip over TB not allow for significantly faster interconnects when clustering Macs?
Why is every computer listing nowadays look the same with the glowing golden and blue chip images and the dynamic images that appear when you scroll down.
Please give me a good old html table with specs will ya?
Funny to wakeup and see this on the front page - I literally just bought a pair last night for work (and play) somewhat on a whim, after comparing the available models. This one was available the soonest & cheapest, CDW is giving 100 off even, so 2900 pre tax.
It's very, very good as an ARM Linux development machine; the Cortex-X925s are Zen5 class (with per-core L2 caches twice as big, even!) and it has a lot of them; the small cores aren't slouches either (around Apple M1 levels of perf IIRC?) GB10 might legitimately be the best high-performance Linux-compatible ARM workstation you can buy right now, and as a bonus it comes with a decent GPU.
A GPU cluster would work better but if you're only testing things out using CUDA and want 200GB networking and somewhat low power all in one this would be the device for you
AI stuff aside I'm frankly happy to see workstation-class AArch64 hardware available to regular consumers.
Last few jobs I've had were for binaries compiled to target ARM AArch64 SBC devices, and cross compiling was sometimes annoying, and you couldn't truly eat your own dogfood on workstations as there's subtle things around atomics and memory consistency guarantees that differ between ISAs.
Mac M series machines are an option except that then you're not running Linux, except in VM, and then that's awkward too. Or Asahi which comes with its own constraints.
Having a beefy ARM machine at my desk natively running Linux would have pleased me greatly. Especially if my employer was paying for it.
These are primarily useful for developing CUDA targeted code on something that sits on your desk and has a lot of RAM.
They're not the best choice for anyone who wants to run LLMs as fast and cheap as possible at home. Think of it like a developer tool.
These boxes are confusing the internet because they've let the marketing teams run wild (or at least the marketing LLMs run wild) trying to make them out to be something everyone should want.
Looks like a pretty useful offering, 128Gb Memory Unified, with the ability to be chained. IN the Uk release price looks to be £2999.99 Nice to see AI Inference becoming available to us all, rather than using a GPU ..3090etc.
You'd have to be doing something where the unified memory is specifically necessary, and it's okay that it's slow. If all you want is to run large LLMs slowly, you can do that with split CPU/GPU inference using a normal desktop and a 3090, with the added benefit that a smaller model that fits in the 3090 is going to be blazing fast compared to the same model on the spark.
Eh, this is way overblown IMO. The product page claims this is for training, and as long as you crank your batch size high enough you will not run into memory bandwidth constraints.
I've finetuned diffusion models streaming from an SSD without noticeable speed penalty at high enough batchsize.
Asus make some really useful things, but the v1 Tinker Board was really a bit problem-ridden, for example. This is similarly way out on the edge of their expertise; I'm not sure I'd buy an out-there Asus v1 product this expensive.
Even cheaper, unless you want the really high-end enterprise stuff. You can run ComfyUI pretty comfy for $0.30 to $0.40 per hour, if AI art is your goal.
1) This still has raster hardware, even ray tracing cores. It's not technically an "AI focused card" like the AMD Instinct hardware or Nvidia's P40-style cards.
2) It kinda does have a stack. ARM is the hardest part to work around, but Box86 will get the older DirectX titles working. The GPU is Vulkan compliant too, so it should be able to leverage Proton/DXVK to accommodate the modern titles that don't break on ARM.
The tough part is the price. I don't think ARM gaming boxes will draw many people in with worse performance at a higher price.
This bit of the FAQ was such a non-answer to their own FAQ, you really have to wonder:
>> What is the memory bandwidth supported by Ascent GX10?
> AI applications often require a bigger memory. With the NVIDIA Blackwell GPU that supports 128GB of unified memory, ASUS Ascent GX10 is an AI supercomputer that enables faster training, better real-time inference, and support larger models like LLMs.
Really interested to see if anyone starts using the fancy high end Connect-X 7 NIC in these DGX Spark / GB10 derived systems. 200Gbit RDMA is available & would be incredible to see in use here.
is this another product they're pushing out for publicity. I mean how much testing has been done for this product. Need more specs and testing results to illuminate capabilities, practicality.
If you touch the image when scrolling on mobile then it opens when you lift your finger. Then when you press the cross in the corner to close the image, the search button behind it is activated.
How can a serious company not notice these glaring issues in their websites?
Taiwanese companies still don't value good software engineering, so talented developers who know how to make money leave. This leaves enterprise darlings like Asus stuck with hiring lower tier talent for numbers that look good to accounting.
From the FAQ… doesn’t seem promising when they ask and then evade a crucial question.
> What is the memory bandwidth supported by Ascent GX10? AI applications often require a bigger memory. With the NVIDIA Blackwell GPU that supports 128GB of unified memory, ASUS Ascent GX10 is an AI supercomputer that enables faster training, better real-time inference, and support larger models like LLMs.
It sounds good, but it ultimately fails to comprehend the question: ignoring the word "bandwidth" and just spewing pretty nonsense.
Which is appropriate, given the applications!
I see that they mention it uses LPDDR5x, so bandwidth will not be nearly as fast as something using HBM or GDDR7, even if bus width is large.
Edit: I found elsewhere that the GB10 has a 256bit L5X-9400 memory interface, allowing for ~300GB/sec of memory bandwidth.
For comparison, the RTX 5090 has a memory bandwidth of 1,792 GB/s. The GX10 will likely be quite disappointing in terms of tokens per second and therefore not well suited for real-time interaction with a state-of-the-art large language model or as a coding assistant.
It doesn't sound good at all. It sounds like malicious evasion and marketing bullshit.
It gives you a very good idea of the capability of the models you'll be running on it!
It doesn't give a good idea of anything. We already know it has 128GB unified memory from the first bullet point on the page.
GP was subtly implying that the text was written by an LLM (running in the very same Ascent GX10).
I think the previous user made a joke about LLMs spewing nonsense on top of AI bs thus this product being quite fitting.
This is a tangent, but the little pop up example for their ai chat bot to try and entice me to use it was something along the lines of “what are the specs?”
How great would it be if instead of shoving these bots to help decipher the marketing speak they just had the specs right up front?
But how would that boost their KPIs for user engagement and AI usage?
Seems this is basically DGX Spark with 1TB of disk so about $1000 bucks cheaper. DGX Spark has not been received well (at least online, Carmack saying it runs at half the spec, low memory bandwidth etc.) so perhaps this is way to reduce buyers regret, you are out only $3000 and not $4000 (with DGX Spark).
Simon Willison seems to like his:https://til.simonwillison.net/llms/codex-spark-gpt-oss
Performance wise it was able to spit out about half of a buggy version of Space Invaders as a single HTML file in roughly a minute.
I’m pretty sure I could spit out something that doesn’t work in half a minute.
"I don't think I'll use this heavily"
Some of the stuff in the Carmack thread made it sound like it could be due to thermals, so maybe could reach or come a lot closer to, but not sustain, and if this has better cooling maybe it does better? I might be off on that.
I ordered one that arrived last week. It seems like a great idea with horrible execution. The UI shows strange glitchy/artifacts occasionally as if there's a hardware failure.
To get a sense for use cases, see the playbooks on this website: https://build.nvidia.com/spark.
Regarding limited memory bandwidth: my impression is that this is part of the onramp for the DGX Cloud. Heavy lifting/production workloads will still need to be run in the cloud.
I wonder why they even added this to the FAQ if they're gonna weasel their way around it and not answer properly?
> What is the memory bandwidth supported by Ascent GX10?
> AI applications often require a bigger memory. With the NVIDIA Blackwell GPU that supports 128GB of unified memory, ASUS Ascent GX10 is an AI supercomputer that enables faster training, better real-time inference, and support larger models like LLMs.
Never seen anything like that before. I wonder if this product page is actually done and was ready to be public?
Maybe they had a local llm write it but the memory bandwidth was too low for a decent answer.
Taiwanese companies are legendary for producing baller hardware with terrible marketing and documentation that answers important questions. It's like those teams don't talk to each other inside the business.
Fortunately, their products are also easy to crack open and probe.
It seamlessly combines Nvidia's price gouging and ASUS's shady tactics. God forbid you ever have to RMA it, they'll probably brake it and blame it on you.
Probably LLM slop, but also it's the same GB10 chip as the DGX Spark so why would the memory bandwidth be significantly different?
How is it different from their consumer GPU marketing? They have Founder Edition under NVIDIA brand initially, but the ecosystem is supposed to mass produce. It appears to be the same for DGX Spark where PNY has produced the NVIDIA branded and now you're going to see ASUS and Dell and others make similar PCs under their brand.
As far as I can tell these are all the same hardware just different enclosures. I'm not sure why Nvidia went this route given that they have a first party device. Usually you only see this when the original manufacturer doesn't want to be in the distribution or support game.
If this is anything like their consumer graphics cards, the first-party version will only be available in the dozen or so countries where Nvidia has established direct distribution channels and they'll defer to the third-parties everywhere else.
Distribution channels to orgs or countries that don't buy from nvidia. Ability to cut discounts w/o discounting the Nvidia brand
One past related thread. Any others?
The Asus Ascent GX10 a Nvidia GB10 Mini PC with 128GB of Memory and 200GbE - https://news.ycombinator.com/item?id=43425935 - March 2025 (50 comments)
It's the same as DGX Spark so there are several:
https://news.ycombinator.com/item?id=45586776
https://news.ycombinator.com/item?id=45008434
https://news.ycombinator.com/item?id=45713835
https://news.ycombinator.com/item?id=45575127
https://news.ycombinator.com/item?id=45611912
https://news.ycombinator.com/item?id=43409281
https://news.ycombinator.com/item?id=45739844
Is there something similar with twice the memory/bandwidth? That's a use case that I would seriously consider to run any frontier open source model locally, at usable speed. 128GB is almost enough.
"Nvidia dgx os", ugh. It would be a lot more enticing if that thing could run stock Linux.
It's just Ubuntu with precanned Nvidia software, otherwise it's a "normal" UEFI + ACPI booting machine, just like any x86 desktop. People have already installed NixOS and Fedora 43, and you can even go ahead and then install CUDA and it will work, too. (You might be able to forgo the nvidia modules and run upstream Mesa+NVK, even.) It's very different from Jetson and much more like a normal x86 desktop.
The kernel is patched (and maintained by Canonical, not Nvidia) but the patches hanging off their 6.17-next branch didn't look outrageous to me. The main hitch right now is that upstream doesn't have a Realtek r8127 driver for the ethernet controller. There were also some mediatek-related patches that were probably relevant as they designed the CPU die.
Overall it feels close to full upstream support (to be clear: you CAN boot this system with a fully upstream kernel, today). And booting with UEFI means you can just use the nvidia patches on $YOUR_FAVORITE_DISTRO and reboot, no need to fiddle with or inject the proper device trees or whatever.
Yeah that's a bummer. They do the same for all their boards like the Jetson Nano.
What would be the advantages, exactly?
it's basically just linux with a custom kernel and cuda preinstalled
I assume the driver code just isn't in mainline linux and installing the correct toolchain isn't always easy. Having it turnkey available is nice and fundamentally new hardware just isn't going to have day 1 linux support.
You're free to lift the kernel and any drivers/libraries and run them on your distribution of choice, it'll just be hacky.
Couldn't you buy a Mac Ultra with more memory for the same price?
This Asus box costs $3000, and the cheapest Mac Studio with the same amount of RAM costs $3500, or $3700 if you also match the SSD capacity.
You do get about twice as much memory bandwidth out of the Mac though.
The resale cost shouldn't be ignored either, that Mac Studio will definitely resell for more than this will by a significant amount. Least of all because the Mac Studio is useful in all kinds of industries whereas this is quite niche.
What's the cheapest way to get the same memory and memory bandwidth as a Mac Studio but also CUDA support?
CUDA is only on nvidia GPUs, I guess a RTX Pro 6000 would get you close, two of them are 192GB in total. Vastly increased memory bandwidth too. Maybe two/four of the older A100/A6000 could do the trick too.
Somehow, it is still cheaper to own 10x RTX 3060s than it is to buy a 120gb Mac.
The Mac will be much smaller and use less power, though.
My reasons for not choosing an Apple product for such a use-case:
1- I vote with my wallet, do I want to pay a company to be my digital overlord, doing everything they can to keep me inside their ecosystem? I put too much effort to earn my freedom to give it up that easily.
2- Software: Almost certainly, I would want to run linux on this. Do I want to have something that has or eventually will have great mainstream linux support, or something with closed specs that people in Asahi try to support with incredible skills and effort? I prefer the system with openly available specs.
I've extensively used mac, iphone, ipad over time. The only apple device I ever bought was an ipad, and I would never buy it, if I knew they deliberately disable multitasking on it.
Cuda is king
Still? Really? Why?
Better support than MPS and nothing Apple is shipping today can compete with even the high end consumer CUDA devices in actual speed.
Presumably the second point is irrelevant if you're choosing among devices with unified memory.
Inertia. Almost everybody else was asleep at the wheel for the last decade and you do not catch up to that kind of sustained investment overnight.
For how shit it all is, it's still the easiest to use, with most available resources when you inevitable need to dig through stuff. Just things like nsight GUI and available debugging options ends up bringing together a better developer experience compared to other ecosystems. I do hope the competitors get better though because the current de facto monopoly helps no-one.
GX10 vs MacBook Pro M4 Max:
- Price: $3k / $5k
- Memory: same (128GB)
- Memory bandwidth: ~273GB/s / 546GB/sec
- SSD: same (1 TB)
- GPU advantage: ~5x-10x depending on memory bottleneck
- Network: same 10Gbe (via TB)
- Direct cluster: 200Gb / 80Gb
- Portable: No / Yes
- Free Mac included: No / Yes
- Free monitor: No / Yes
- Linux out of the box: Yes / No
- CUDA Dev environment: Yes : No
> Free monitor: No / Yes
How is the monitor "free" if the Mac costs more?
On the networking side. M4 max does have thunderbolt 5, 80gbps advertised. Would ip over TB not allow for significantly faster interconnects when clustering Macs?
Yes, people use Thundebolt networking to build Mac AI clusters. The Spark has 200G Ethernet that is even faster though.
Made the correction to 80Gb/sec thank you.
W.r.t ip, the fastest I’m aware of is 25Gb/s via TB5 adapters like from Sonnet.
You should not be using an adapter to get IP over Thunderbolt. Just connect a Thunderbolt5 cable to both machines.
Why is every computer listing nowadays look the same with the glowing golden and blue chip images and the dynamic images that appear when you scroll down.
Please give me a good old html table with specs will ya?
But the ai chatbot popup suggests you can conversationally ask for the specs
Funny to wakeup and see this on the front page - I literally just bought a pair last night for work (and play) somewhat on a whim, after comparing the available models. This one was available the soonest & cheapest, CDW is giving 100 off even, so 2900 pre tax.
I presume this is not yet in your possession. Please do let us know how it goes.
Nope not shipped/processed yet even. It was listed as in stock with a realistic number though!
Any good ideas for what these can be used for?
I am still trying to think a use case that a Ryzen AI Max/MacBook or a plain gaming gpu cannot cover.
It's very, very good as an ARM Linux development machine; the Cortex-X925s are Zen5 class (with per-core L2 caches twice as big, even!) and it has a lot of them; the small cores aren't slouches either (around Apple M1 levels of perf IIRC?) GB10 might legitimately be the best high-performance Linux-compatible ARM workstation you can buy right now, and as a bonus it comes with a decent GPU.
Laptop-class bandwidth without that annoying portability.
A GPU cluster would work better but if you're only testing things out using CUDA and want 200GB networking and somewhat low power all in one this would be the device for you
AI stuff aside I'm frankly happy to see workstation-class AArch64 hardware available to regular consumers.
Last few jobs I've had were for binaries compiled to target ARM AArch64 SBC devices, and cross compiling was sometimes annoying, and you couldn't truly eat your own dogfood on workstations as there's subtle things around atomics and memory consistency guarantees that differ between ISAs.
Mac M series machines are an option except that then you're not running Linux, except in VM, and then that's awkward too. Or Asahi which comes with its own constraints.
Having a beefy ARM machine at my desk natively running Linux would have pleased me greatly. Especially if my employer was paying for it.
These are primarily useful for developing CUDA targeted code on something that sits on your desk and has a lot of RAM.
They're not the best choice for anyone who wants to run LLMs as fast and cheap as possible at home. Think of it like a developer tool.
These boxes are confusing the internet because they've let the marketing teams run wild (or at least the marketing LLMs run wild) trying to make them out to be something everyone should want.
Looks like a pretty useful offering, 128Gb Memory Unified, with the ability to be chained. IN the Uk release price looks to be £2999.99 Nice to see AI Inference becoming available to us all, rather than using a GPU ..3090etc.
https://www.scan.co.uk/products/asus-ascent-gx10-desktop-ai-...
All Sparks only have a memory bandwidth of 270 GB/s though (about the same as the Ryzen AI Max+ 395), while the 3090 has 930 GB/s.
(Edit: GB of course, not MB, thanks buildbot)
The 3090 also has 24gb of ram vs 128gb for the spark
You'd have to be doing something where the unified memory is specifically necessary, and it's okay that it's slow. If all you want is to run large LLMs slowly, you can do that with split CPU/GPU inference using a normal desktop and a 3090, with the added benefit that a smaller model that fits in the 3090 is going to be blazing fast compared to the same model on the spark.
Eh, this is way overblown IMO. The product page claims this is for training, and as long as you crank your batch size high enough you will not run into memory bandwidth constraints.
I've finetuned diffusion models streaming from an SSD without noticeable speed penalty at high enough batchsize.
I believe you mean GB/s?
I would hold my horses and see if the specs are actually true and not overblown like for the spark otherwise there are better options.
This is a Spark, so it is not going to be any different.
And if waiting six months is possible, do that.
Asus make some really useful things, but the v1 Tinker Board was really a bit problem-ridden, for example. This is similarly way out on the edge of their expertise; I'm not sure I'd buy an out-there Asus v1 product this expensive.
I really wish I had the kind of money to try my hands at it.
You can rent GPUs from many providers for a few bucks an hour.
Even cheaper, unless you want the really high-end enterprise stuff. You can run ComfyUI pretty comfy for $0.30 to $0.40 per hour, if AI art is your goal.
What a shame. This would have been a much more powerful machine if it was wrapped around AMD products.
At least with this, you get to pay both the Nvidia and the Asus tax!
In this case the Asus "tax" is negative $1,000.
Does anyone have any information on how much this will cost? Or is it one of those products where if you have to ask you can't afford it.
Lots of existing posts in this discussion talking about prices in various regions and configurations.
Which models will this be able to run at an acceptable token/s rate?
gpt-oss:120b
https://til.simonwillison.net/llms/codex-spark-gpt-oss
Am I missing it or is there no information about performance? Looking for a tokens/sec
He didn't give that info but the transcript linked at the end shows how much time was spent for each query.
These AI boxes resemble gaming consoles in both form factor and architecture, makes me curious if they could make good gaming machines.
Likely not. Bit like the AI focused cards get their ass kicked by much cheaper gaming cards. The focus has diverged
Plus ofc software stack for gaming on this isn’t available
Eh, I wouldn't be so hasty:
1) This still has raster hardware, even ray tracing cores. It's not technically an "AI focused card" like the AMD Instinct hardware or Nvidia's P40-style cards.
2) It kinda does have a stack. ARM is the hardest part to work around, but Box86 will get the older DirectX titles working. The GPU is Vulkan compliant too, so it should be able to leverage Proton/DXVK to accommodate the modern titles that don't break on ARM.
The tough part is the price. I don't think ARM gaming boxes will draw many people in with worse performance at a higher price.
This bit of the FAQ was such a non-answer to their own FAQ, you really have to wonder:
>> What is the memory bandwidth supported by Ascent GX10?
> AI applications often require a bigger memory. With the NVIDIA Blackwell GPU that supports 128GB of unified memory, ASUS Ascent GX10 is an AI supercomputer that enables faster training, better real-time inference, and support larger models like LLMs.
> This bit of the FAQ was such a non-answer to their own FAQ, you really have to wonder:
You don't have to wonder: I bet they're using generative AI to speed up delivery velocity.
[delayed]
How much does that thing cost? I don't see a price on the page.
Really interested to see if anyone starts using the fancy high end Connect-X 7 NIC in these DGX Spark / GB10 derived systems. 200Gbit RDMA is available & would be incredible to see in use here.
is this another product they're pushing out for publicity. I mean how much testing has been done for this product. Need more specs and testing results to illuminate capabilities, practicality.
I was really hyped about this, but then I watched videos and it's just meh.
What is the purpose of this thing?
If you touch the image when scrolling on mobile then it opens when you lift your finger. Then when you press the cross in the corner to close the image, the search button behind it is activated.
How can a serious company not notice these glaring issues in their websites?
Taiwanese companies still don't value good software engineering, so talented developers who know how to make money leave. This leaves enterprise darlings like Asus stuck with hiring lower tier talent for numbers that look good to accounting.
AI powered business value provider frontend developers.
On desktop, clicking on an image opens it but then you can't close it, and the zoom seems to be glitchy.
But I'm not surprised, this is ASUS. As a company, they don't really seem to care about software quality.
Enshittification.
Its not that they dont notice.
They dont care.