HP ZGX Fury Is Now Orderable: GB300 Superchip, 748GB Unified Memory(storagereview.com) |
HP ZGX Fury Is Now Orderable: GB300 Superchip, 748GB Unified Memory(storagereview.com) |
I think you might need a bit more than that at this price point ...
One listing is 410K for 280 sq/ft coming out at £1464 per square foot, almost exactly 10x the price per sq/ft we paid for our house a few years ago.
So $100K would be £74K which would get you ~50 sq/ft.
They weren't trying to steal it, they just wanted to bring it over to their side. But the lock did its job! This is the perfect situation for a Kensington lock.
I yelled at them the next day telling them if they need a Series 5 'scope they could go rent or buy one.
https://www.gigabyte.com/in/Enterprise/Tower-Server/W775-V10...
Basically it's the same as a tray in a GB300 NVL72, but in workstation/desktop form. Niche would be AI researchers.
They will... not sell a lot of these. But what a beast.
I am too lazy to price out 496GB of DDR5 but, um, mostly because it is terrifying to see what today's prices look like.
You can't build such a machine out yourself but if you could I suspect the price point would come out about the same.
That's kind of the nature of capitalism and price elasticity - the manufacture price only limited the minimum sale price, and sale price usually reflects how much is the market willing to pay for the good.
Where you might save a lot of money is on building something that's very targeted to your needs that matches them better than a GB300 workstation, for a lower price.
I don't get what this device is for.
I a million percent understand wanting 252GB-VRAM, that would get me a 280-320B model like glm-5.3 or deepseek-v4-flash, which would be a massive improvement over my gpt-oss:20b 16GB toy. I would gladly pay a grand for this, I would never pay ten grand for this, and it seems to be priced around a hundred grand.
So obviously the customer is commercial not consumer.
Can anyone planning a project around one of these at work share what their workload is shaped like and how they're modeling price/performance?
For instance, I don't get the 512GB of system memory, I'd gladly drop that to 128 to save money. Am I missing something about commercial workloads? Is a 1T parameter model at 20 tok/s more important to your workload than a 300B one at 60? Is it simply a co-dependency of not the model but the other software you're running on the machine thats using/interacting-with/being-driven-by the model?
Whats your napkin math to justify 100k? Actually thats not even really the question, its more like - whats your napkin math to determine between the "dual linked GB10" use case vs this product's use case vs an 8U supermicro with 4 cards use case.
https://www.guru3d.com/story/nvidia-dgx-spark-achieves-175-f...
- 252 GB HBM3e VRAM
- 496 GB LPDDR5X RAM
It just doesn’t make sense to buy this stuff at peak prices.
So it has only 252GB of actual ”AI” memory making it “useless”/toy for actual real world AI workloads(I.e it can’t replace something like opus 5)
It's a workstation, not a rack. It's for AI researchers. I'd love to have one on (err, under) my desk.
What even is this comment?
It’s not useless but it is for “real work”. Apple has been providing 512GB ram machines for several years so to say you need a rack to run a large model for personal usage it’s missing the point.
You needed a rack of nvidia cards to match an 128GB ram Mac as well a while ago so it’s more of the same.
What is it with those stupid names?
No price, so of course this is not for the smelly working class.
It's an AI research workstation for people whose ultimate work goes on production GB300 NVL72 data centre racks (and for large models you link them together.)
It's also about 15x the memory bandwidth of a Mac. For models that fit you'd be looking at hundreds of tokens per second on decode and prefill many times that.
And has CUDA, which is (likely) what your production system will use.
And runs a real server operating system.
Also by the time you spec'd out a Mac with the same total memory capacity and computation you'd also be as expensive. And still not have as many cores, nor have the ConnectX RDMA networking speeds.
You could build out a complicated multi GPU setup of your own, but the work you do on inference tuning for your kernels etc would not necessarily translate to the real world.
But yes, if you just want to run GLM 5.3 on your own machine, that's a whole other story.
Exactly. It's tailor-built to be a GB300 tray under your desk. Unless you really need a GB300 tray under your desk, you would be better served looking elsewhere.
We could condition datacenter installs to providing power to the grid - you want to build a datacenter, you also need to build a wind or solar farm that can fully power its peak load.