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Nvidia DGX Spark: great hardware, early days for the ecosystem
- ChrisArchitect 1y agoMore discussion: https://news.ycombinator.com/item?id=45575127 https://news.ycombinator.com/item?id=45575127
- ur-whale 1y agoAs is usual for NVidia: great hardware, an effing nightmare figuring out how to setup the pile of crap they call software.
- p_l 1y agoAnd yet CUDA has looked way better than ATi/AMD offerings in the same area despite ATi/AMD technically being first to deliver GPGPU (major difference is that CUDA arrived year later but supported everything from G80 up, and nicely evolved, while AMD managed to have multiple platforms with patchy support and total rewrites in between)
- cylemons 1y agoWhat was the AMD GPGPU called?
- p_l 1y agoWhich one? We first had the flurry of third party work (Brook, Lib Sh, etc), then we had AMD "Close to Metal" which was IIRC based on Brook, soon followed with dedicated cards, year later we got CUDA (also derived partially from Brook!) and AMD Stream SDK, later renamed APP SDK. Then we got HIP / HSA stuff which unfortunately has its biggest legacy (outside of availability of HIP as way to target ROCm and CUDA simultaneously) in low level details of how GPU game programming evolved on Xbox360 / PS4 / XBox One / PS5. Somewhere in between AMD seemed to bet on OpenCL, yet today with latest drivers from both AMD and nVidia I get more OpenCL features on nVidia. And of course there's the part of totally random and inconsistent support outside of the few dedicated cards, which is honestly why CUDA the de facto standard everyone measures against - you could run CUDA applications, if slowly, even on the lowest end nvidia cards, like Quadro NVS series (think lowest end GeForce chip but often paired with more displays and different support that focused on business users that didn't need fast 3D). And you still can, generally, run core CUDA code within last few generations on everything from smallest mobile chip to biggest datacenter behemoth.
- pjmlp 1y agoYou forgot the C++AMP collaboration with Microsoft.
- p_l 1y agoIs it the OpenMP related one or another thing? I kinda lost track, this whole thread reminded me how hopeful I was to play with GPGPU with my then new X1600
- pjmlp 1y agoOther thing, https://learn.microsoft.com/en-us/cpp/parallel/amp/cpp-amp-cpp-accelerated-massive-parallelism?view=msvc-170 https://learn.microsoft.com/en-us/cpp/parallel/amp/cpp-amp-c...
- kanwisher 1y agoIf you think their software is bad try using any other vendor , makes nvidia looks amazing. Apple is only one close
- enoch2090 1y agoAlthough a bit off the GPU topic, I think Apple's Rosetta is the smoothest binary transition I've ever used.
- stefan_ 1y agoKeep in mind this is part of Nvidias embedded offerings. So you will get one release of software ever, and that's gonna be pretty much it for the lifetime of the product.
- Grimblewald 1y agoPretty much this. Nvidia isn't big because of their hardware, they're not ahead on that front. It's their software support that makes it worthwhile.
- jasonjmcghee 1y agoExcept the performance people are seeing is way below expectations. It seems to be slower than an M4. Which kind of defeats the purpose. It was advertised as 1 Petaflop on your desk. But maybe this will change? Software issues somehow? It also runs CUDA, which is useful
- airstrike 1y agoit fits bigger models and you can stack them. plus apparently some of the early benchmarks were made with ollama and should be disregarded
- pjmlp 1y agoTry to use Intel or AMD stuff instead.
- triwats 1y agoFascinating to me managing some of these systems just how bad the software is. Management becomes layers upon layers of bash scripts which ends up calling a final batch script written by Mellanox. They'll catch up soon, but you end up having to stay strictly on their release cycle always. Lots of effort.
- simonw 1y agoIt's notable how much easier it is to get things working now that the embargo has lifted and other projects have shared their integrations. I'm running VLLM on it now and it was as simple as: docker run --gpus all -it --rm \ --ipc=host --ulimit memlock=-1 \ --ulimit stack=67108864 \ nvcr.io/nvidia/vllm:25.09-py3 (That recipe from https://catalog.ngc.nvidia.com/orgs/nvidia/containers/vllm?version=25.09-py3 https://catalog.ngc.nvidia.com/orgs/nvidia/containers/vllm?v... ) And then in the Docker container: vllm serve & vllm chat The default model it loads is Qwen/Qwen3-0.6B, which is tiny and fast to load.
- behnamoh 1y agoI'm curious, does its architecture support all CUDA features out of the box or is it limited compared to 5090/6000 Blackwell?
- 3abiton 1y agoAs someone who hot on early on the Ryzen AI 395+, are there any added value for the DGX Spark beside having cuda (compared to ROCm/vulkan)? I feel Nvidia fumbled the marketing, either making it sound like an inference miracle, or a dev toolkit (then again not enough to differentiate it from the superior AGX Thor). I am curious about where you find its main value, and how would it fit within your tooling, and use cases compared to other hardware? From the inference benchmarks I've seen, a M3 Ultra always come on top.
- storus 1y agoM3 Ultra has slow GPU and no HW FP4 support so its initial token decoding is going to be slow, practically unusable for 100k+ context sizes. For token generation that is memory bound M3 Ultra would be much faster, but who wants to wait 15 minutes to read the context? Spark will be much faster for initial token processing, giving you a much better time to first token, but then 3x slower (273 vs 800GB/s) in token generation throughput. You need to decide what is more important for you. Strix Halo is IMO the worst of both worlds at the moment due to having the worst specs in both dimensions and the least mature software stack.
- matt3210 1y ago> even in a Docker container I should be allowed to do stupid things when I want. Give me an override!
- simonw 1y agoA couple of people have since tipped me off that this works around that: IS_SANDBOX=0 claude --dangerously-skip-permissions You can run that as root and Claude won't complain.
- fulafel 1y agoIf you want to run stuff in Docker as root, better enable uid remapping, since otherwise the in-container uid 0 is still the real uid 0 and weakens the security boundary of the containerization. (Because Docker doesn't do this as by default, best practice is to create a non root user in your dockerfile and run as that)
- simonw 1y agoCorrection: it's IS_SANDBOX=1
- fnordpiglet 1y agoThis seems to be missing the obligatory pelican on a bicycle.
- simonw 1y agoHere's one I made with it - I didn't include it in the blog post because I had so many experiments running that I lost track of which model I'd used to create it! https://tools.simonwillison.net/svg-render#%3Csvg%20width%3D%22300%22%20height%3D%22200%22%20xmlns%3D%22http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg%22%3E%0A%20%20%3C!--%20Bicycle%20Frame%20--%3E%0A%20%20%3Cg%20stroke%3D%22black%22%20stroke-width%3D%222%22%20fill%3D%22none%22%3E%0A%20%20%20%20%3C!--%20Seat%20post%20--%3E%0A%20%20%20%20%3Cline%20x1%3D%22150%22%20y1%3D%22100%22%20x2%3D%22150%22%20y2%3D%2250%22%20%2F%3E%0A%20%20%20%20%3C!--%20Seat%20--%3E%0A%20%20%20%20%3Crect%20x%3D%22140%22%20y%3D%2245%22%20width%3D%2220%22%20height%3D%2210%22%20%2F%3E%0A%20%20%20%20%3C!--%20Handlebars%20--%3E%0A%20%20%20%20%3Cline%20x1%3D%22100%22%20y1%3D%2260%22%20x2%3D%22180%22%20y2%3D%2260%22%20%2F%3E%0A%20%20%20%20%3C!--%20Wheels%20--%3E%0A%20%20%20%20%3Ccircle%20cx%3D%22100%22%20cy%3D%22130%22%20r%3D%2220%22%20%2F%3E%0A%20%20%20%20%3Ccircle%20cx%3D%22180%22%20cy%3D%22130%22%20r%3D%2220%22%20%2F%3E%0A%20%20%20%20%3C!--%20Frame%20lines%20--%3E%0A%20%20%20%20%3Cline%20x1%3D%22150%22%20y1%3D%2250%22%20x2%3D%22100%22%20y2%3D%22130%22%20%2F%3E%0A%20%20%20%20%3Cline%20x1%3D%22150%22%20y1%3D%2250%22%20x2%3D%22180%22%20y2%3D%22130%22%20%2F%3E%0A%20%20%3C%2Fg%3E%0A%0A%20%20%3C!--%20Pelican%20Body%20--%3E%0A%20%20%3Ccircle%20cx%3D%22150%22%20cy%3D%2260%22%20r%3D%2215%22%20fill%3D%22gray%22%20%2F%3E%0A%20%20%3C!--%20Pelican%20Head%20--%3E%0A%20%20%3Ccircle%20cx%3D%22150%22%20cy%3D%2245%22%20r%3D%2210%22%20fill%3D%22brown%22%20%2F%3E%0A%20%20%3C!--%20Beak%20--%3E%0A%20%20%3Cpolygon%20points%3D%22150%2C45%20160%2C40%20155%2C50%22%20fill%3D%22orange%22%20%2F%3E%0A%20%20%3C!--%20Eyes%20--%3E%0A%20%20%3Ccircle%20cx%3D%22145%22%20cy%3D%2242%22%20r%3D%222%22%20fill%3D%22white%22%20%2F%3E%0A%20%20%3Ccircle%20cx%3D%22148%22%20cy%3D%2242%22%20r%3D%221%22%20fill%3D%22black%22%20%2F%3E%0A%20%20%3C!--%20Wings%20--%3E%0A%20%20%3Cpath%20d%3D%22M135%2C60%20C120%2C40%20120%2C70%20135%2C60%22%20fill%3D%22none%22%20stroke%3D%22gray%22%20stroke-width%3D%222%22%20%2F%3E%0A%20%20%3Cpath%20d%3D%22M165%2C60%20C180%2C40%20180%2C70%20165%2C60%22%20fill%3D%22none%22%20stroke%3D%22gray%22%20stroke-width%3D%222%22%20%2F%3E%0A%20%20%3C!--%20Legs%20--%3E%0A%20%20%3Cline%20x1%3D%22150%22%20y1%3D%2275%22%20x2%3D%22145%22%20y2%3D%2290%22%20stroke%3D%22black%22%20stroke-width%3D%222%22%20%2F%3E%0A%20%20%3Cline%20x1%3D%22150%22%20y1%3D%2275%22%20x2%3D%22155%22%20y2%3D%2290%22%20stroke%3D%22black%22%20stroke-width%3D%222%22%20%2F%3E%0A%3C%2Fsvg%3E https://tools.simonwillison.net/svg-render#%3Csvg%20width%3D...
- two_handfuls 1y agoI wonder how this compares financially with renting something on the cloud.
- killingtime74 1y agoFor me as an employee in Australia, I could buy this and write it off my tax as a work expense myself. To rent, it would be much more cumbersome, involving the company. That's 45% off (our top marginal tax rate).
- Grimburger 1y ago> That's 45% off (our top marginal tax rate) Can people please not listen to this terrible advice that gets repeated so oft, especially in Australian IT circles somehow by young naive folks. You really need to talk to your accountant here. It's probably under 25% in deduction at double the median wage, little bit over @ triple, and that's *only* if you are using the device entirely for work, as in it sits in an office and nowhere else, if you are using it personally you open yourself up to all sorts of drama if and when the ATO ever decides to audit you for making a $6k AUD claim for a computing device beyond what you normally to use to do your job.
- lukeh 1y agoAlso, you can only deduct it in a single financial year if you are eligible for the Instant asset write-off program. I'm sure I'll get downvoted for this, but this common misunderstanding about tax deductions does remind me of a certain Seinfeld episode :) Kramer: It's just a write off for them Jerry: How is it a write off? Kramer: They just write it off Jerry: Write it off what? Kramer: Jerry all these big companies they write off everything Jerry: You don't even know what a write off is Kramer: Do you? Jerry: No. I don't Kramer: But they do and they are the ones writing it off
- killingtime74 1y agoCorrect. You can deduct over multiple years, so you do get the same amount back.
- fisian 1y agoThe reported 119GB vs. 128GB according to spec is because 128GB (1e9 bytes) equals 119GiB (2^30 bytes).
- simonw 1y agoUgh, that one gets me every time!
- wmf 1y agoThat can't be right because RAM has always been reported in binary units. Only storage and networking use lame decimal units.
- deleted 1y ago[deleted]
- simonw 1y agoLooks like Claude reported it based on this: ● Bash(free -h) ⎿ total used free shared buff/cache available Mem: 119Gi 7.5Gi 100Gi 17Mi 12Gi 112Gi Swap: 0B 0B 0B That 119Gi is indeed gibibytes, and 119Gi in GB is 128GB.
- deleted 1y ago[deleted]
- wtallis 1y agoYou're barking up the wrong tree. Nobody's manufacturing power-of-ten sized DRAM chips for NVIDIA; the amount of memory physically present has to be 128GiB. If `free` isn't reporting that much usable capacity, you need to dig into the kernel logs to see how much is being reserved by the firmware and kernel and drivers. (If there was more memory missing, it could plausibly be due to in-band ECC, but that doesn't seem to be an option for DGX Spark.)
- rgovostes 1y agoI'm hopeful this makes Nvidia take aarch64 seriously for Jetson development. For the past several years Mac-based developers have had to run the flashing tools in unsupported ways, in virtual machines with strange QEMU options.
- reenorap 1y agoIs 128 GB of unified memory enough? I've found that the smaller models are great as a toy but useless for anything realistic. Will 128 GB hold any model that you can do actual work with or query for answers that returns useful information?
- simonw 1y agoThere are several 70B+ models that are genuinely useful these days. I'm looking forward to GLM 4.6 Air - I expect that one should be pretty excellent, based on experiments with a quantized version of its predecessor on my Mac. https://simonwillison.net/2025/Jul/29/space-invaders/ https://simonwillison.net/2025/Jul/29/space-invaders/
- behnamoh 1y agothe question is: how does the prompt processing time on this compare to M3 Ultra because that one sucks at RAG even though it can technically handle huge models and long contexts...
- zozbot234 1y agoPrompt processing time on Apple Silicon might benefit from making use of the NPU/Apple Neural Engine. (Note, the NPU is bad if you're limited by memory bandwidth, but prompt processing is compute limited.) Just needs someone to do the work.
- magicalhippo 1y agoDepending on you use-case, I've been quite impressed with GPT-OSS 20B with high reasoning effort. The 120B model is better but too slow since I only have 16GB VRAM. That model runs decent[1] on the Spark. [1]: https://news.ycombinator.com/item?id=45576737 https://news.ycombinator.com/item?id=45576737
- cocogoatmain 1y ago128gb unified memory is enough for pretty good models, but honestly for the price of this it is better just go go with a few 3090s or a Mac due to memory bandwidth limitations of this card
- monster_truck 1y agoWhole thing feels like a paper launch being held up by people looking for blog traffic missing the point. I'd be pissed if I paid this much for hardware and the performance was this lacklustre while also being kneecapped for training
- rubatuga 1y agoWhen the networking is 25GB/s and the memory bandwidth is 210GB/s you know something is seriously wrong.
- TiredOfLife 1y agoIt has connectx 200GB/s
- wtallis 1y agoNo, the NIC runs at 200Gb/s, not 200GB/s.
- _ache_ 1y agoWhat do you mean by "kneecapped for training"? Isn't it 128GB of VRAM enougth for small model training, that a current GC can't do? Obviously, even with connectx, it's only 240Gi of VRAM, so no big models can be trained.
- monster_truck 1y agoSpend some time looking at the real benchmarks before writing nonsense
- _ache_ 1y agoYou are quite rude here. I was asking questions. The benchmarks are very new and don't explains why it can used for training. But if FP4 means 4bit floating point, and that the hardware capability of the DGX Spark is effectively only in FP4, then yes. That was nonsense to wish it could have been used for training. But it wasn't obvious from the advertising of nvidia.
- jhcuii 1y agoDespite the large video memory capacity, its video memory bandwidth is very low. I guess the model's decode speed will be very slow. Of course, this design is very well suited for the inference needs of MoE models.
- rcarmo 1y agoAbout what I expected. The Jetson series had the same issues, mostly, at a smaller scale: Deviate from the anointed versions of YOLO, and nothing runs without a lot of hacking. Being beholden to CUDA is both a blessing and a curse, but what I really fear is how long it will take for this to become an unsupported golden brick. Also, the other reviews I’ve seen point out that inference speed is slower than a 5090 (or on par with a 4090 with some tailwind), so the big difference here (other than core counts) is the large chunk of “unified” memory. Still seems like a tricky investment in an age where a Mac will outlive everything else you care to put on a desk and AMD has semi-viable APUs with equivalent memory architectures (even if RoCm is… well… not all there yet). Curious to compare this with cloud-based GPU costs, or (if you really want on-prem and fully private) the returns from a more conventional rig.
- EnPissant 1y agoThis thing is dramatically slower than a 4090 both in prefill and decode. And I do mean DRAMATICALLY. I have no immediate numbers for prefill, but the memory bandwidth is ~4x greater on a 4090 which will lead to ~4x faster decode.
- KeplerBoy 1y agoThis is kind of an embedded 5070 with a massive amount of relatively slow memory, don't expect miracles.
- TiredOfLife 1y agoNo need to put unified in scare quotes.
- ZiiS 1y agoGiven the likelihood you are bound by the 4x lower memory bandwidth this implies; at least for decode, I think they are warranted.
- 3abiton 1y ago> Also, the other reviews I’ve seen point out that inference speed is slower than a 5090 (or on par with a 4090 with some tailwind), so the big difference here (other than core counts) is the large chunk of “unified” memory. It's not comparable to 4090 inference speed. It's significantly slower, because of the lack of MXFP4 models out there. Even compared to Ryzen AI 395 (ROCm / Vulkan), on gpt-oss-120B mxfp4, somehow DGX manages to lose on token generation (pp is faster though. > Still seems like a tricky investment in an age where a Mac will outlive everything else you care to put on a desk and AMD has semi-viable APUs with equivalent memory architectures (even if RoCm is… well… not all there yet). ROCm (v7) for APUs came a long way actually, mostly thanks to the community effort, it's quite competitive and more mature. It's still not totally user friendly, but it doesn't break between updates (I know the bar is low, but that was the status a year ago). So in comparison, the strix halo offers lots of value for your money if you need a cheap compact inference box. Havn't tested finetuning / training yet, but in theory it's supported, not to forget that APU is extremely performany for "normal" tasks (threadripper level) compared to the CPU of the DGX Spark.
- saagarjha 1y agoI’m kind of surprised at the issues everyone is having with the arm64 hardware. PyTorch has been building official wheels for several months already as people get on GH200s. Has the rest of the ecosystem not kept up?
- deleted 1y ago[deleted]
- smallnamespace 1y agoAn 14-inch M4 Max Macbook Pro with 128GB of RAM has a list price of $4700 or so and twice the memory bandwidth. For inference decode the bandwidth is the main limitation so if running LLMs is your use case you should probably get a Mac instead.
- dialogbox 1y agoWhy Macbook Pro? Isn't Mac Studio is a lot cheaper and the right one to compare with DGX Spark?
- AndroTux 1y agoI think the idea is that instead of spending an additional $4000 on external hardware, you can just buy one thing (your main work machine) and call it a day. Also, the Mac Studio isn’t that much cheaper at that price point.
- MomsAVoxell 1y agoBeing able to leave the thing at home and access it anywhere is a feature, not a bug. The Mac Studio is a more appropriate comparison. There is not yet a DGX laptop, though.
- AndroTux 1y ago> Being able to leave the thing at home and access it anywhere is a feature, not a bug. I can do that with a laptop too. And with a dedicated GPU. Or a blade in a data center. I though the feature of the DGX was that you can throw it in a backpack.
- MomsAVoxell 1y agoThe DGX is clearly a desktop system. Sure, it's luggable. But the point is, it's not a laptop.
- 1y ago
- physicsguy 1y agoA few years ago I worked on an ARM supercomputer, as well as a POWER9 one. x86 is so assumed for anything other than trivial things that it is painful. What I found was a good solution was using Spack: https://spack.io/ https://spack.io/ That allows you to download/build the full toolchain of stuff you need for whatever architecture you are on - all dependencies, compilers (GCC, CUDA, MPI, etc.), compiled Python packages, etc. and if you need to add a new recipe for something it is really easy. For the fellow Brits - you can tell this was named by Americans!!!
- donw 1y agoWho says we don’t have a sense of humor.
- physicsguy 1y agoIt's that it's an offensive term here, not a funny one.
- MomsAVoxell 1y agoAussie checking in, smokos over, get back to work...
- theowaway 1y ago[dead]
- teleforce 1y agoIt's good that you've mentioned Spack but not for HPC work, and that's very interesting. This a high level overview by one of the Spack authors from the HN post back in 2023 (top comment from 100 comments), including the Spack original paper link [1]: At a very high level, Spack has: * Nix's installation model and configuration hashing * Homebrew-like packages, but in a more expressive Python DSL, and with more versions/options * A very powerful dependency resolver that doesn't just pick from a set of available configurations -- it configures your build according to possible configurations. You could think of it like Nix with dependency resolution, but with a nice Python DSL. There is more on the "concretizer" (resolver) and how we've used ASP for it here: * "Using Answer Set Programming for HPC Dependency Solving", https://arxiv.org/abs/2210.08404 https://arxiv.org/abs/2210.08404 [1] Spack – scientific software package manager for supercomputers, Linux, and macOS (100 comments): https://news.ycombinator.com/item?id=35237269 https://news.ycombinator.com/item?id=35237269
- _joel 1y agoHow would this fare alongside the new Ryzen chips, ooi? From memory is seems to be getting the same amount of tok/s but would the Ryzen box be more useful for other computing, not just AI?
- KeplerBoy 1y agoIf you need x86 or windows for anything it's not even a question.
- _joel 1y agoSure, Mac's are also arm based, my question was about general performance, not architecture
- justincormack 1y agoFrom reading reviews, dont have either yet: the nvidia actually has unified memory, AMD you have to specify the allocation split. Nvidia maybe has some form of gpu partitioning so you can run multiple smaller models but no one got it working yet. The Ryzen is very different from the pro gpus and the software support wont benefit from work done there, while nvidia is same. You can play games on Ryzen.
- blurbleblurble 1y agoBut on the ryzen the vram allocation can be entirely dynamically allocated. I saw a review showing excellent full GPU usage during inference with the bios vram allocation set to the minimum level, using a very large model. So it's not so simple as you describe (I used to think this was the case too). Beyond that, seems like the 395 in practice smashes the dgx spark in inference speeds for most models. I haven't seen nvfp4 comparisons yet and would be very interested to.
- justincormack 1y agoYes you can set it but in the BIOS, not dynamically as you need it. I dont think there are any models supporting nvfp4 yet but we shall probably start seeing them.
- amelius 1y ago> x86 architecture for the rest of the machine. Can anyone explain this? Does this machine have multiple CPU architectures?
- B1FF_PSUVM 1y agoI went looking for pictures (in the photo the box looked like a tray to me ...) and found an interesting piece by Canonical touting their Ubuntu base for the OS: https://canonical.com/blog/nvidia-dgx-spark-ubuntu-base https://canonical.com/blog/nvidia-dgx-spark-ubuntu-base P.S. exploded view from the horse's mouth: https://www.nvidia.com/pt-br/products/workstations/dgx-spark/_jcr_content/root/responsivegrid/nv_container_copy_2118561310/nv_image.coreimg.100.1290.jpeg/1742557581678/nvidia-project-digits-exploded-vew-ari-22.jpeg https://www.nvidia.com/pt-br/products/workstations/dgx-spark...
- rvz 1y agoTLDR: Just buy a RTX 5090. The DGX Spark is completely overpriced for its performance compared to a single RTX 5090.
- _ache_ 1y agoI get the idea. But isn't 128G of "VRAM" (unified actually) could train a usefull ViT model ? I don't think the 5090 could do that with only 32G of VRAM, couldn't it ?
- storus 1y agoDGX Spark is not for training, only for inference (FP4).
- sailingparrot 1y agoIts a DGX dev box, for those (not consumers) that will ultimately need to run their code on large DGX clusters where a failure or a ~3% slowdown of training ends up costing tens of thousands of dollars. That's the use case, not running LLM efficiently, and you can't do that with a RTX5090.
- storus 1y agoIs ASUS Ascent GX10 and similar from Lenovo etc. 100% compatible with DGX Spark and can be chained together with the same functionality (i.e. ASUS together with Lenovo for 256GB inference)?
- solarboii 1y agoAre there any benchmarks comparing it with the Nvidia Thor? It is much more available than spark, and performance might not be very different
- andy99 1y agoIs there like an affiliate link or something where I can just buy one? Nvidia’s site says sold out, PNY invites you to find a retailer, the other links from nvidia didn’t seem to go anywhere. Can one just click to buy it somewhere?
- BoredPositron 1y agoMy local reseller has them in stock in the EU with a markup... Directly from Nvidia probably not for quite sometime I have some friends who put in preorders and they didn't get any from the first charge.
- roughsquare 1y agoIt still isn't at distributors yet. My distributor has it listed for Oct 27, with units shipping the day after from the warehouse to resellers/etc.
- triwats 1y agoAdded this to my benchmark site as seems that we might see a lot of purpose build desktop systems going forward. You CAN build - but for people wanting to get started this could be a real viable option. Perhaps less so though with Apple's M5? Let's see... https://flopper.io/gpu/nvidia-dgx-spark https://flopper.io/gpu/nvidia-dgx-spark