[HN Gopher] Asus Ascent GX10
___________________________________________________________________
Asus Ascent GX10
Author : jimexp69
Score : 180 points
Date : 2025-11-10 15:56 UTC (7 hours ago)
(HTM) web link (www.asus.com)
(TXT) w3m dump (www.asus.com)
| simlevesque wrote:
| I really wish I had the kind of money to try my hands at it.
| hamdingers wrote:
| You can rent GPUs from many providers for a few bucks an hour.
| uyzstvqs wrote:
| 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.
| maxbaines wrote:
| Looks like a pretty useful offering, 128Gb Memory Unified, with
| the ability to be chained. IN the Uk release price looks to be
| PS2999.99 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-...
| BoredPositron wrote:
| 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.
| exasperaited wrote:
| 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.
| eightysixfour wrote:
| This is a Spark, so it is not going to be any different.
| atwrk wrote:
| 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)
| buildbot wrote:
| I believe you mean GB/s?
| postalrat wrote:
| The 3090 also has 24gb of ram vs 128gb for the spark
| Gracana wrote:
| 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.
| Jackson__ wrote:
| 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.
| cmxch wrote:
| At that price (roughly 4000 USD), one could build a full HBM
| powered Xeon system from the Sapphire Rapids generation.
|
| Either build a single socket system and give it some DDR5 to
| work alongside, or go dual socket and a bit less DDR5 memory.
| npalli wrote:
| 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).
| cma wrote:
| 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.
| nxobject wrote:
| I'd love to see how far shucking it and using aftermarket
| cooling will go. Or perhaps it's hard-throttled for market
| segmentation purposes?
| simlevesque wrote:
| Simon Willison seems to like
| his:https://til.simonwillison.net/llms/codex-spark-gpt-oss
| colordrops wrote:
| "I don't think I'll use this heavily"
| jandrese wrote:
| 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.
| badgersnake wrote:
| I'm pretty sure I could spit out something that doesn't
| work in half a minute.
| jandrese wrote:
| Don't undersell it. The game is playable in a browser.
| The graphics are just blocks, the aliens don't return
| fire. There are no bunkers. The aliens change colors when
| they descend to a new level (whoops). But for less than
| 60 seconds of effort it does include the aliens (who do
| properly go all the way to the edges, so the strategy of
| shooting the sides off of the formation still works--not
| every implementation gets that part right), and it does
| detect when you have won the game. The tank and the
| bullets work, and it even maintains the limit on the
| number of bullets you can have in the air at once.
| However, the bullets are not destroyed by the aliens so a
| single shot can wipe out half of a column. It also
| doesn't have the formation speed up as you destroy the
| aliens.
|
| So it is severely underbaked but the base gameplay is
| there. Roughly what you would expect out of a LLM given
| only the high level objective. I would expect an hour or
| so of vibe coding would probably result in something
| reasonably complete before you started bumping up into
| the context window. I'm honestly kind of impressed that
| it worked at all given the minuscule amount of human
| input that went into that prompt.
| JohnBooty wrote:
| I do think that people typically undersell the ability of
| LLMs as coding assistants!
|
| I'm not quite sure how impressed to be by the LLM's
| output here. Surely there are quite a few simple Space
| Invaders implementations that made it into the training
| corpus. So the amount of work the LLM did here may have
| been relatively small; more of a simple regurgitation?
|
| What do you think?
| ofalkaed wrote:
| >The aliens change colors when they descend to a new
| level (whoops).
|
| That is how Space Invaders originally worked, used strips
| of colored cellophane to give the B&W graphics color and
| the aliens moved behind a different colored strip on each
| level down. So, maybe not an whoops?
|
| Edit: After some reading, I guess it was the second
| release of Space Invaders which had the aliens change
| color as they dropped, first version only used the
| cellophane for a couple parts of the screen.
| npalli wrote:
| I think this is the key, it can do impressive stuff but it
| won't be fast. For that, you have to put in a NVidia data
| center / AI Factory.
| BoredPositron wrote:
| He likes everything.
| npalli wrote:
| He is very enthusiastic about new things but even he
| struggled (for ex. the first link is about his experience OOB
| with Sparq and it wasn't a smashing success).
| Should you get one? # It's a bit too early for me to
| provide a confident recommendation concerning this machine.
| As indicated above, I've had a tough time figuring out how
| best to put it to use, largely through my own inexperience
| with CUDA, ARM64 and Ubuntu GPU machines in general.
| The ecosystem improvements in just the past 24 hours have
| been very reassuring though. I expect it will be clear within
| a few weeks how well supported this machine is going to be.
| sirlancer wrote:
| Except Carmack, as much as I hate to say it, was simply wrong.
| If you run the GPU at full throttle then you get the power draw
| that he reported. However, if you run the CPU AND the GPU at
| full throttle, then you can draw all the power that's
| available.
| buildbot wrote:
| 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.
| binary132 wrote:
| I presume this is not yet in your possession. Please do let us
| know how it goes.
| buildbot wrote:
| Nope not shipped/processed yet even. It was listed as in
| stock with a realistic number though!
| nik736 wrote:
| Which models will this be able to run at an acceptable token/s
| rate?
| simlevesque wrote:
| gpt-oss:120b
|
| https://til.simonwillison.net/llms/codex-spark-gpt-oss
| hamdingers wrote:
| Am I missing it or is there no information about performance?
| Looking for a tokens/sec
| simlevesque wrote:
| He didn't give that info but the transcript linked at the
| end shows how much time was spent for each query.
| aseipp wrote:
| Right now I get 59 tok/sec on GPT-OSS 120B using Unsloth's
| dynamic 4-bit quants, via llama.cpp
| https://news.ycombinator.com/item?id=45881049
| brian_herman wrote:
| Couldn't you buy a Mac Ultra with more memory for the same price?
| simlevesque wrote:
| Cuda is king
| MangoToupe wrote:
| Still? Really? Why?
| baby_souffle wrote:
| 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.
| embedding-shape wrote:
| 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.
| whywhywhywhy wrote:
| Better support than MPS and nothing Apple is shipping today
| can compete with even the high end consumer CUDA devices in
| actual speed.
| MangoToupe wrote:
| Presumably the second point is irrelevant if you're
| choosing among devices with unified memory.
| bigyabai wrote:
| It is not. Unified memory is not a panacea, it says
| nothing about the compute performance of the hardware.
|
| The Spark's GPU gets ~4x the FP16 compute performance of
| an M3 Ultra GPU on less than half the Mac Studio's total
| TDP.
| MangoToupe wrote:
| right, but that doesn't describe a "high end consumer
| CUDA device". Nothing under that description has unified
| memory.
| bigyabai wrote:
| _Every_ CUDA-compatible GPU has had support for unified
| memory since 2014:
| https://developer.nvidia.com/blog/unified-memory-cuda-
| beginn...
|
| Can you be a bit more specific what technology you're
| actually referring to? "Unified memory" is just a
| marketing term, you could mean unified address space,
| dual-use memory controllers, SOC integration or
| Northbridge coprocessors. All are technologies that
| Nvidia has shipped in consumer products at one point or
| another, though (Nintendo Switch, Tegra Infotainment,
| 200X MacBook to name a few).
| nl wrote:
| They mean the ability to run a large model entirely on
| the GPU without paging it out of a separate memory
| system.
| bigyabai wrote:
| They're basically describing the Jetson and Tegra lineup,
| then. Those were featured in several high-end consumer
| devices, like smart-cars and the Nintendo Switch.
| nl wrote:
| Sure but neither had enough memory to be useful for large
| LLMs.
|
| And neither were really consumer offerings.
| jsheard wrote:
| 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.
| chrsw wrote:
| What's the cheapest way to get the same memory and memory
| bandwidth as a Mac Studio but also CUDA support?
| embedding-shape wrote:
| 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.
| bigyabai wrote:
| Somehow, it is still cheaper to own 10x RTX 3060s than it
| is to buy a 120gb Mac.
| woodson wrote:
| The Mac will be much smaller and use less power, though.
| bigyabai wrote:
| Would almost be a no-brainer if the Mac GPU wasn't a
| walled garden.
| tuna74 wrote:
| Is that any different from nVidia?
| Someone1234 wrote:
| 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.
| brian_herman wrote:
| Oh thanks for clarifing!
| aljgz wrote:
| 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.
| dbtc wrote:
| Not disagreeing with any of your points, but this is a good
| trend right?
|
| https://github.com/apple/container
|
| > container is a tool that you can use to create and run
| Linux containers as lightweight virtual machines on your Mac.
| It's written in Swift, and optimized for Apple silicon.
| bigyabai wrote:
| That would have been an impressive piece of technology in
| 2015, when WSL was theoretical. To release it in _2025_ is
| a very bad trend, and it reflects Apple 's isolation from
| competition and reluctance to officially support basic dev
| features.
|
| Container does nothing to progress the state of supporting
| Linux on Apple Silicon. It does not replace macOS, iBoot or
| the other proprietary, undocumented or opaque software
| blobs on the system. All it does is keep people using macOS
| and purchasing Apple products and viewing Apple
| advertisements.
| 7734128 wrote:
| 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?
| tomalaci wrote:
| AI powered business value provider frontend developers.
| the_real_cher wrote:
| Enshittification.
|
| Its not that they dont notice.
|
| They dont care.
| janlukacs wrote:
| but it has AI in it.
| speedgoose wrote:
| 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.
| schainks wrote:
| 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.
| cbsmith wrote:
| 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.
| palmotea wrote:
| > 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.
| cbsmith wrote:
| I guess that's the kindest possible interpretation. The other
| interpretation is that the answer is not a good one.
| abtinf wrote:
| 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.
| LeifCarrotson wrote:
| 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.
| guerrilla wrote:
| It doesn't sound good at all. It sounds like malicious
| evasion and marketing bullshit.
| exe34 wrote:
| It gives you a very good idea of the capability of the
| models you'll be running on it!
| guerrilla wrote:
| 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.
| epolanski wrote:
| I think the previous user made a joke about LLMs spewing
| nonsense on top of AI bs thus this product being quite
| fitting.
| darkwater wrote:
| GP was subtly implying that the text was written by an
| LLM (running in the very same Ascent GX10).
| BikiniPrince wrote:
| With a little tinkering we can just have the AI gaslight
| us about it's capabilities.
| guerrilla wrote:
| Ah! Thanks for explaining. haha
| tuhgdetzhh wrote:
| 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.
| curvaturearth wrote:
| Written by a LLM?
| Youden wrote:
| They seem to have another FAQ here that gives a real answer
| (273GB/s): https://www.asus.com/us/support/faq/1056142/
| suprjami wrote:
| Now we can see why they avoided giving a straight answer.
|
| File this one in the blue folder like the DGX
| fancyfredbot wrote:
| They have failed to provide answers to other FAQ as well. The
| answers are really awkward and don't read like LLM output which
| I'd expect to be much more fluent. Perhaps a model which was
| lobotomized through FP4 quantisation and "fine tuning" on one
| of these.
| embedding-shape wrote:
| 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?
| skrebbel wrote:
| Maybe they had a local llm write it but the memory bandwidth
| was too low for a decent answer.
| porphyra wrote:
| Probably LLM slop, but also it's the same GB10 chip as the DGX
| Spark so why would the memory bandwidth be significantly
| different?
| baby_souffle wrote:
| 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.
| jsheard wrote:
| 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.
| jonfw wrote:
| Distribution channels to orgs or countries that don't buy
| from nvidia. Ability to cut discounts w/o discounting the
| Nvidia brand
| tgma wrote:
| 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.
| moffkalast wrote:
| 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.
| schainks wrote:
| 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.
| LtdJorge wrote:
| Also terrible software and firmware. Examples are the
| programs for motherboard RGB control from Asus, Asrock, MSI,
| Gigabyte, etc.
| joelthelion wrote:
| "Nvidia dgx os", ugh. It would be a lot more enticing if that
| thing could run stock Linux.
| porphyra wrote:
| it's basically just linux with a custom kernel and cuda
| preinstalled
| simlevesque wrote:
| Yeah that's a bummer. They do the same for all their boards
| like the Jetson Nano.
| colechristensen wrote:
| 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.
| CamperBob2 wrote:
| What would be the advantages, exactly?
| CamperBob2 wrote:
| Guess I have my answer.
| aseipp wrote:
| 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.
| BoredPositron wrote:
| I got burned more than once with Nvidia not providing kernel
| updates straight after release...
| aseipp wrote:
| That was also my experience with their Jetson series [1],
| but my understanding is that these DGX kernels are not
| maintained by Nvidia but by Canonical, so they operate
| directly out of their package repos and on Canonicals'
| release and support schedule (e.g. 24.04 supported until
| 2029.) You can already get 6.14 from the package repos, and
| 6.17 can be built from source and is regularly updated if
| you follow the Git repositories. It's also not like the
| system is unusable without patches, and I suspect most will
| go upstream.
|
| Based on my experience it feels quite different and much
| closer to a normal x86 machine, probably intentional. Maybe
| it helped that Nvidia did not design the full CPU complex,
| Mediatek did that.
|
| [1] They even claim that Thor is now fully SBSA compliant
| (Xavier had UEFI, Orin had better UEFI, and now this) --
| which would imply it has full UEFI + ACPI like the Spark.
| But when I looked at the kernel in their Thor L4T release,
| it looked like it was still loaded with Jetson-specific SOC
| drivers on top of a heavy fork of the PREEMPT_RT patch
| series for Linux 6.8; I did not look too hard, but it still
| didn't seem ideal. Maybe you can probably boot a "normal"
| OS missing most of the actual Jetson-specific peripherals,
| I guess.
| blmarket wrote:
| Wait, x86? you mean arm64?
| aseipp wrote:
| It's a bit ambiguous but I can't edit now, sorry. What I
| meant to say was that it boots using the same mechanism as
| x86 machines that you are familiar with, not that it is an
| x86 machine itself.
| 9front wrote:
| DGXOS is a customized Ubuntu Noble!
|
| /etc/os-release: PRETTY_NAME="Ubuntu 24.04.3
| LTS" NAME="Ubuntu" VERSION_ID="24.04"
| VERSION="24.04.3 LTS (Noble Numbat)"
| VERSION_CODENAME=noble ID=ubuntu ID_LIKE=debian
| HOME_URL="https://www.ubuntu.com/"
| SUPPORT_URL="https://help.ubuntu.com/"
| BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/"
| PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-
| policies/privacy-policy" UBUNTU_CODENAME=noble
| LOGO=ubuntu-logo
|
| and /etc/dgx-release: DGX_NAME="DGX Spark"
| DGX_PRETTY_NAME="NVIDIA DGX Spark"
| DGX_SWBUILD_DATE="2025-09-10-13-50-03"
| DGX_SWBUILD_VERSION="7.2.3" DGX_COMMIT_ID="833b4a7"
| DGX_PLATFORM="DGX Server for KVM" DGX_SERIAL_NUMBER="Not
| Specified"
|
| While other Linux distros were already reported to work, some
| tools provide by Nvidia won't work with Fedora or NixOS. Not
| yet!
|
| I couldn't get Nvidia AI Workbench to start on Neon KDE after
| changing to DISTRIB_ID=Ubuntu in /etc/lsb-release. Neon is
| based on Ubuntu Noble too.
| Stevvo wrote:
| These AI boxes resemble gaming consoles in both form factor and
| architecture, makes me curious if they could make good gaming
| machines.
| Havoc wrote:
| 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
| bigyabai wrote:
| 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.
| vinkelhake wrote:
| That would depend on your idea of "good". It would be an
| upstream swim in most regards, but you could certainly make it
| work. The Asahi team has shown that you can get steam working
| pretty well on ARM based machines.
|
| But if gaming is what you're actually interested in, then it's
| a pretty terrible buy. You can get a much cheaper x86-based
| system with a discrete GPU that runs circles around this.
| whatever1 wrote:
| 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.
| MurkyLabs wrote:
| 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
| cmrdporcupine wrote:
| 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.
| addaon wrote:
| Laptop-class bandwidth without that annoying portability.
| aseipp wrote:
| 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.
| sneilan1 wrote:
| 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.
| sbarre wrote:
| Lots of existing posts in this discussion talking about prices
| in various regions and configurations.
| DiabloD3 wrote:
| 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!
| wmf wrote:
| In this case the Asus "tax" is negative $1,000.
| jauntywundrkind wrote:
| 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.
| WhitneyLand wrote:
| 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
| tassadarforaiur wrote:
| 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?
| WhitneyLand wrote:
| 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.
| tgma wrote:
| You should not be using an adapter to get IP over
| Thunderbolt. Just connect a Thunderbolt5 cable to both
| machines.
| WhitneyLand wrote:
| For point to point sure, but if you want to connect
| multiple machines in an actual fabric you'll need some
| kind of network interop.
|
| The Asus clustering speed is not limited to p2p.
| tgma wrote:
| Fair enough. On the other hand you have more thunderbolts
| to make up a clique mesh of seven point to point Macs.
| wmf wrote:
| Yes, people use Thundebolt networking to build Mac AI
| clusters. The Spark has 200G Ethernet that is even faster
| though.
| josefresco wrote:
| > Free monitor: No / Yes
|
| How is the monitor "free" if the Mac costs more?
| bigyabai wrote:
| > Linux out of the box: Yes / No
|
| For homelab use, this is the only thing that matters to me.
| hasperdi wrote:
| AMD 395+ is more bang for the buck IMO.
|
| GMKtec EVO-X2 vs GX10 vs MacBook Pro M4 Max
| Price: $2,199.99 / $3,000 / $5,000 CPU: Ryzen AI Max
| 395+ (Strix Halo, 16C/32T) / NVIDIA Grace Blackwell GB200
| Superchip (20-core ARM v9.2) / Apple M4 Max (12C) GPU:
| Radeon 890M (RDNA3 iGPU) / Integrated Blackwell GPU (up to 1
| PFLOP FP4) / 40-core integrated GPU Memory: 128GB
| LPDDR5X / 128GB LPDDR5X unified / 128GB unified Memory
| bandwidth: ???GB/s / ~500GB/s / ~546GB/s SSD: 1TB PCIe
| 4.0 / 4TB PCIe 5.0 / 1TB NVMe GPU advantage: Similar
| (EVO-X2 trades blows with GB10 depending on model and
| framework) Network: 2.5GbE / 10GbE / 10GbE (via TB)
| Direct cluster: 40Gb (USB4/TB4) / 200Gb / 80Gb Portable:
| Semi (compact desktop) / No / Yes Free Mac included: No
| / No / Yes Free monitor: No / No / Yes Linux out
| of the box: Yes / Yes / No CUDA dev environment: No
| (ROCm) / Yes / No
| Aurornis wrote:
| 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.
| mahirsaid wrote:
| 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.
| dinkleberg wrote:
| 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?
| arcanemachiner wrote:
| But how would that boost their KPIs for user engagement _and_
| AI usage?
| mey wrote:
| Why not burn down some tree's and show the wrong information
| instead of putting a simple table?
| yndoendo wrote:
| I find all these Popup Assistant Bots as bad User Experience.
|
| No, I don't want to use your assistant and your are forcing me
| to pointlessly click on the close button. Some times they event
| hide viable information during their popup.
|
| They seem to be the reincarnation of 2000s popups; there to
| satisfy a business manager versus actually being a useful tool.
| varispeed wrote:
| I was really hyped about this, but then I watched videos and it's
| just meh.
|
| What is the purpose of this thing?
| oblio wrote:
| How much does that thing cost? I don't see a price on the page.
| irusensei wrote:
| 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?
| malfist wrote:
| But the ai chatbot popup suggests you can conversationally ask
| for the specs
| dang wrote:
| 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)
|
| Edit: added via wmf's comment below:
|
| _" DGX Spark has only half the advertised performance"_ -
| https://news.ycombinator.com/item?id=45739844 - Oct 2025 (24
| comments)
|
| _Nvidia DGX Spark: When benchmark numbers meet production
| reality_ - https://news.ycombinator.com/item?id=45713835 - Oct
| 2025 (117 comments)
|
| _Nvidia DGX Spark and Apple Mac Studio = 4x Faster LLM Inference
| with EXO 1.0_ - https://news.ycombinator.com/item?id=45611912 -
| Oct 2025 (20 comments)
|
| _Nvidia DGX Spark: great hardware, early days for the ecosystem_
| - https://news.ycombinator.com/item?id=45586776 - Oct 2025 (111
| comments)
|
| _NVIDIA DGX Spark In-Depth Review: A New Standard for Local AI
| Inference_ - https://news.ycombinator.com/item?id=45575127 - Oct
| 2025 (93 comments)
|
| _Nvidia DGX Spark_ -
| https://news.ycombinator.com/item?id=45008434 - Aug 2025 (207
| comments)
|
| _Nvidia DGX Spark_ -
| https://news.ycombinator.com/item?id=43409281 - March 2025 (10
| comments)
| wmf wrote:
| 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
| dang wrote:
| Thanks! Added above.
| nycdatasci wrote:
| 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.
| tcdent wrote:
| The graphics company has given up on graphics.
| lend000 wrote:
| 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.
| wmf wrote:
| Mac Studio
| bigyabai wrote:
| Even an M3 Ultra won't put up similar GPU compute to a DGX
| Spark: https://blog.exolabs.net/nvidia-dgx-spark/
|
| Fill up the memory with a large model, and most of your
| memory bandwidth will be waiting on compute shaders. Seems
| like a waste of $5,000 but you do you.
| wmf wrote:
| I should also mention that if you want twice the performance of
| DGX Spark you can buy... two Sparks and link them together.
| sparkler123 wrote:
| I had one of these on pre-order/reservation from when they
| announced the DGX Spark and ended up returning it after a couple
| days. I thought I'd give it a shot, though. The 128GB of unified
| memory was the big selling point (as are any of the DGX Spark
| boxes), but the memory bandwidth was very disappointing. Being
| able to load a 100B+ parameter model was cool in terms of novelty
| but not particularly great for local inferencing.
|
| Also, NVIDIA's software they have you install on another machine
| to use it is garbage. They tried to make it sort of appliance-y
| but most people would rather just have SSH work out of the box
| and can go from there. IMO just totally unnecessary. The software
| aspect was what put me over the edge.
|
| Maybe the gen 2 will be better, but unless you have a really
| specific use case that this solves well, buy credits or something
| somewhere else.
| mirekrusin wrote:
| I have a weird feeling that "Spark 2" may have an apple logo on
| it.
| qwertox wrote:
| My hope was to find a system which does ASR, then LLM processing
| with MCP use and finally TTS: "Put X on my todo list" / "Mark X
| as done" -> LLM thinks, reads the todo list, edits the todo list,
| and tells me "I added X to your todo list", ... "Turn all the
| lights off" -> llm thinks and uses MCP to turn off the lights ->
| "Lights have been turned off". "Send me an email at 8pm reminding
| me to do" .... "Email has been scheduled for 8pm"
|
| That's all I want. It does not have to be fast, but it must be
| capable of doing all of that.
|
| Oh, and it should be energy efficient. Very important for a 24/7
| machine.
| bayindirh wrote:
| Energy efficient LLM inference (for now) is as realistic as
| existence of perpetual motion.
| qwertox wrote:
| I wouldn't mind if it burns 200 watts while it does the task,
| as long as it idles at below 30W
| bayindirh wrote:
| NVIDIA H200 idles at 75 watts. I'm not keeping my hopes
| high on that, either.
| janstice wrote:
| To be fair, if I paid $30k+ for an H200, I'd want it to
| be making money 24/7 rather than idling, so the idle
| power draw would be strictly theoretical.
| mindcrash wrote:
| You can already do that on most desktop GPU's (even going as
| far as prev gen Nv 1050/1060/1070 for example).
|
| You'll need a model able to work with tools, like llama 3.2
| (https://huggingface.co/meta-llama), serve it, hook up MCPs,
| include a STT interface, and you're cooking.
| bayindirh wrote:
| Even a bottom of the barrel N95 has audio acceleration
| features helping with speech to text, but the LLM inference
| part still will be far from being efficient.
|
| Plus, you need to keep the card at "ready" state, you can't
| idle/standby it completely.
| nl wrote:
| You probably can do this now. Non-generative LLMs don't need to
| be as big so something like Gemma 4B on the CPU will work.
|
| You may have better results with semi-templated responses
| though.
| mindcrash wrote:
| ServeTheHome has already benchmarked the DGX Spark architecture
| against the (very obvious) Ryzen AI Max 395+ with 128G RAM:
|
| https://www.servethehome.com/nvidia-dgx-spark-review-the-gb1...
|
| If (and in case of Nvidia that's a big if at the moment) they get
| their software straight on Linux for once this piece of hardware
| seems to be something to keep an eye on.
| canucker2016 wrote:
| GMKtec, maker of the EVO-X2 mini-PC that uses a Ryzen AI Max
| 395+, posted a blog post with a comparison between the DGX
| Spark and their EVO-X2 miniPC.
|
| from https://www.gmktec.com/blog/evo-x2-vs-nvidia-dgx-spark-
| redef... (text taken from https://wccftech.com/forget-nvidia-
| dgx-spark-amd-strix-halo-... since the GMKtec table was an
| image, but wccftech converted to an HTML table - EDIT-
| reformatted to make table look nicer in monospace font w/o
| tabs) Test Model Metric
| EVO - X2 NVIDIA GB10 Winner Llama 3.3 70B
| Generation Speed (tok/sec) 4.90 4.67
| AMD First Token Response Time (s) 0.86
| 0.53 NVIDIA Qwen3 Coder Generation Speed
| (tok/sec) 35.13 38.03 NVIDIA
| First Token Response Time (s) 0.13 0.42
| AMD GPT-OSS 20B Generation Speed (tok/sec) 64.69
| 60.33 AMD First Token Response
| Time (s) 0.19 0.44 AMD Qwen3 0.6B
| Model Generation Speed (tok/sec) 163.78 174.29
| NVIDIA First Token Response Time (s)
| 0.02 0.03 AMD
| mindcrash wrote:
| And additionally Framework apparently benchmarked GPT-OSS
| 120B (!) on the maxed out 395+ Desktop and reached a 38.0
| tok/sec Generation Speed. Given that Nvidia can't even keep
| up on a 20B model, I assume they can't keep up on the 120B
| model aswell.
|
| https://frame.work/nl/en/desktop?tab=machine-learning
|
| So to me the only thing which seems to be interesting about
| the Spark atm is the ability to daisy link several units
| together so you can create a InfiniBand-ish network at
| InfiniBand speeds of Sparks.
|
| But overall for just plain development and experimentation,
| and since I don't work at Big AI, I'm pretty sure I would not
| purchase Nvidia at the moment.
| aseipp wrote:
| Unfortunately comparing tok/sec right now in a vacuum and
| especially across weeks of time is kind of pointless.
| Everything is still evolving; there were patches within
| days that bumped GB10 performance by double digit
| percentiles in some frameworks. You just kind of have to
| accept things are a moving target.
|
| For comparison, as of right now, I can run GPT-OSS 120b @
| 59 tok/sec, using llama.cpp (revision 395e286bc) and
| Unsloth dynamic 4-bit quantized models.[1] GPT-OSS 20b @ 88
| tok/sec [2]. The MXFP4 variant comes in the same, at ~89
| tok/sec[3]. It's probably faster on other frameworks,
| llama.cpp is known to not be the fastest. I don't know what
| LM Studio backend they used. All of these numbers put the
| GB10 well ahead of Strix Halo, if only going by the numbers
| we see here.
|
| If the AMD software wasn't also comparatively optimized by
| the same amount in the same timeframe, then the GB10 would
| be faster, now. Maybe it was optimized just as much; I
| don't have a Strix Halo part to compare. But my point is,
| don't just compare numbers from two various points in time,
| it's going to be very misleading.
|
| [1]: https://huggingface.co/unsloth/gpt-
| oss-120b-GGUF/tree/main/U... [2]:
| https://huggingface.co/unsloth/gpt-
| oss-20b-GGUF/resolve/main... [3]:
| https://huggingface.co/unsloth/gpt-
| oss-20b-GGUF/resolve/main...
| nl wrote:
| These are valid points but the numbers are still useful
| as a floor on performance.
|
| Given Strix Halo is so much cheaper I'd expect more
| people to work on improving it, but the NVIDIA tools are
| better so unclear which has more headroom.
| aseipp wrote:
| Yeah that's fair. 60 tok/sec on a gpt-oss-120b is
| certainly nice to know if you should even think about it
| at all. I'm quite happy with it anyway.
|
| The pricing is definitely by far the worst part of all of
| this. I suspect the GB10 still has more perf left on the
| table, Blackwell has been a rough launch. But I'm not
| sure it's $2000 better if you're just looking to get a
| fun little AI machine to do embeddings/vision/LLMs on?
| RachelF wrote:
| These very narrow speed measurements are getting out of hand:
|
| 1 petaFLOP using FP4, that's 4 petaFLOPS using FP1 and infinite
| petaFLOPS using FP0.
| amelius wrote:
| > and support larger models like LLMs
|
| To turn your petaFLOP into petaSLOP.
| NSUserDefaults wrote:
| Really looking forward to getting this used for $50 in 6 years
| just for kicks.
| canucker2016 wrote:
| Dell and Lenovo have product pages for their versions of the DGX
| Spark.
|
| Dell:
|
| https://www.dell.com/en-us/shop/desktop-computers/dell-pro-m...
|
| - $3,998.99 4TB SSD
|
| - $3,699.00 2TB SSD
|
| Lenovo:
|
| https://www.lenovo.com/us/en/p/workstations/thinkstation-p-s...
|
| - $3,999.00 4TB SSD
|
| https://www.lenovo.com/us/en/p/workstations/thinkstation-p-s...
|
| - $3,539.00 1TB SSD
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