Who needs something like this? What is your job? What do you use it for?

Really curious. I deploy code to a large production server cluster, my friends in academia submit code to large scientific computing clusters, but I don't know of anyone with this much power in their own desktop. I guess I've been in video edit suites with machines much more powerful than the average PC, but not this crazy.

Security reviews. Powerful enough to run multiple VMs to simulate caching server, web server, app server, and database tier while simultaneously recording and as necessary tampering with all network communications while running static and dynamic code inspection tools across multiple VMs. Load it up with some high-end GPUs as well for hash cracking. Memory is a big deal as well. I work with some programs that if you are running anything under 64 GB of RAM you could be waiting all day for results and 256 GB is preferred but can still be sketchy as that may not leave enough for VMs. If you are a researcher doing fuzzing, again, this sort of power is key to reduce a fuzzing run from maybe a month or two on a a high-end laptop to a few minutes.

Running inadequate machines waste time of otherwise expensive engineers. The other day I was giving a live demo of an interception proxy and accidentally clicked on a very large HTTP response. The machine I was using only had 32 GB of RAM so I had to open task manager and kill the app so I could re-start it because the meeting would have been over by the time it loaded due to disk caching.

Do you need it to be local for GUIs, or for confidentiality reasons, or it's just cheaper? I assumed most people needing beefy VM hosts would remotely access servers in datacenters (whether cloud or company-owned).

Probably for cheaper and latency.

Latency, speed, and not wanting to impact people over the wider WAN on reviews which may generate large amounts of traffic.

My usage is for building IaaS software. With a machine like this, I can have VMs with VTX passthrough turned on, and simulate a cloud, without a rack of physical servers.

As a remote employee it allows me to most of my development locally, and then use larger environments for a shorter period of time later on.

Combine 3 or 4 of them, and I can have an actual cloud running under my desk, for testing things like kubernetes deployments, with enough capacity for a few concurrent test environments.

As I said above they run basically silent, so I can use them in a shared office space without annoying my neighbors.

I've been building out cloud IaaS demos (VMware, KVM, the works) for a decade now and since the beginning I've always needed basically the most amount of RAM and cores that could be shoved into a single physical box. Otherwise, I'd wind up swapping to disk or doing double-nested VMs with mere MBs of RAM and that was horrific before SSDs were cost-effective either. Nobody else besides doing weather simulations / HPC or video editing seems to need this much compute either.

I've been hearing of people buying Mac Pros for cloud demos so they can run VMs on them locally because they're among the most compact, compute and memory dense systems you can buy.

Interesting, thanks for sharing. My instinct would have been to rent a few dedicated boxes from a OVH for something like that. What made you decide to go with local boxes?

Personally, I find local dev much easier.

I had access to racks of hardware to do a lot of testing but they were in Colorado, while I am in EU which made latency a real problem.

You see workstations like this in what I'd call "interactive simulation." Stuff where you're designing something complex via simulation and want to be able to rapidly evaluate changes. For example ANSYS (hybrid finite element simulation and CAD) works best on a single workstation and you want it to be fast so you can make quick changes. Or when your geometry is so complex it doesn't fit in any reasonable amount of memory and your code doesn't support distributing it (which is very hard for a lot of simulation types). The (Sauber? I think HP sponsors them) F1 car from the image in the article is a good example of someone who might one one of these.

A lot of battle-tested engineering software doesn't do distributed-memory parallelism well. I've personally used MCNP and GEANT4 (particle physics software) on a 48 core, 1 TB RAM workstation. It's interesting browsing the web while the computer is running gigantic calculations in the background on two [physical] CPUs, hooray for the process scheduler doing a good job.

My desktop is a 2690v4 with 128GB RAM. As a computational physicist I have access to a number of supercomputers to do larger simulations on, but queue time on them can be a hassle for 'smaller' jobs that a regular desktop can't handle. So having a decent middle ground machine is very useful for low resolution testing of jobs you'll throw at the supercomputer later.

I use a 2x12 core Xeon v2, 128GB RAM. I work full time on OS kernels, mainly FreeBSD. It helps a ton for building kernels, world, packages, and running lots of VMs. Thinking of doing the Talos II as the next step.

Imagine doing web dev in a language where you refresh the webpage to see if it worked vs having to run a long build. The former is better, even though it's not really as good as careful reading of your changes, but there are many times throughout the day when you just want the compiler to bark at you to clear any stupid type or syntax errors. The faster your machine and more parallel the build, the closer you get to that web dev experience.

I'm an OpenShift Consultant for Red Hat. Super awesome to be able to run or build from scratch a 9 node HA OpenShift cluster without incurring an AWS or other infrastructure costs.

May I suggest compiling software? For people without access to distributed build and test a box like this is really nice. I do have access to a huge distributed build farm but even then I use a beefy HP workstation because the software that drives the build farm is itself a bit of a pig, and massively multithreaded.

I do 'small data' analysis and having a machine powerful enough to not have to deal with clusters is a godsend and saves me so much time. With 64 gigs and a dozen cores most of my needs are handled. And if I had 128 gb and 24 cores I'd be able to do even more.

To do FPGA synthesis. It takes ages to finish a proper place and route.

Compiling C++ codebases. I have a 170 KLOC codebase that compiles in 20 minutes on a single core. Ideally you'd have at least 40 cores to make changing headers bearable at all.

I've got a workstation - not quite that spec but not far off it. I work in game development, and we work on a multiplayer game. Being able to run multiple instances of the game is crucial, (game server, plus 2,3,4 clients, sometimes with rendering, sometimes without). On top of that, our codebase is gigantic, and takes about 40 minutes to build on our workstations (slowly growing). On my i7 at home, it takes well over 2 hours.

Not a user of such behemoths myself, but in my organisation we've got a lot of GIS people and a few design/architect folks that work a lot with huge datasets, complex models etc that require a fairly large amount of horsepower.

It's funny the things that need a lot of beans to run; I was sitting with one of the GIS guys and he just clicked a button which then caused all 24 cores on his rig to flatline for about 4 minutes. The end result? A map of our city suddenly had a few boxes drawn over the top of it. As it turns out he was pulling in data from half a dozen sources to map out areas where there was government run housing that experienced higher than average levels of emergency services call outs. Apparently, in the past doing that manually would probably have taken several people about a week to do and wouldn't have been as accurate.

I don't have one, but I nearly every day wish I had. I work on database, currently a lot of performance stuff. It's really annoying to do performance work on consumer HW - not enough memory (32GB is the biggest I have seen in portable laptops, and that's what I have) for interesting loads, regular thermal throttling screwing up benchmarks, single socket systems having drastically different performance characteristics than bigger NUMA ones...

Having access to machines remotely can replace some of that, but it definitely costs time.

Usually, engineering simulations (finite element analysis, etc)

It lets me defer the inevitable Chrome Tab Bankruptcy. But seriously, having a heavy build system with lots of dependencies will easily max out a desktop like this.

Hahaha. I'm like that too. But there's another ceiling around 16GB of RAM usage by Chrome processes (almost 200 tabs). :)

dreamworks gives their artists machines like these so they can see the results of physics simulations on their animations in real-time while eduting instead of waiting for a bake step including things like hair and cloth

I used one a couple of years ago for image processing. TIFF stitching workloads were still a "go get a cup of coffee" timescale but it was amazingly fast for everything else.

I use a similiar machine as a workstation for FEI's AMIRA. Actually have about 400 GB of RAM.

Basically 3D rendering.

Seconded - HP Z series are very good quality workstations - very less noise and great Linux compatibility. You can find them cheap as HP refurbs if you want warranty like new, otherwise eBay has them for quite cheap too.

That's really impressive. How many DIMMS and spinning HDDs is it cooling?

Only 8x8GB since I'm working on CPU limited simulation tasks. One of the CPUs and 4 of the DIMMs is on a daughter board in the Z640, which makes the whole thing even more impressive. It's a fairly compact machine. Storage is just a single SSD and single HDD. Graphics is a low end Quadro just to get the 4x mini-DisplayPort. The HP Z8xx series would probably be a better choice if a person is going to go crazy on the rest of the hardware. But that's just conjecture on my part based on the fact that my current processors are running near TDP (but aren't being throttled down based on the monitoring I have done.)

I have a z820 32 cores, 128GB of RAM, 3 nVidia GeForce GT 710 cards, and 3 x 5TB drives. You're not missing much with the z640. I have a z620 as well. The z620 just has 2 fewer PCI-E slots. The difference is the same for the z840 and z640.

Can you explain the 3x Nvidia 710s? The only reasoning I can think of is running ~6 monitors silently, but the heatsink-only cards aren't usually used in prebuilts so I'm a little confused.

Exactly. 6 monitors in 3x2, they're super cost effective at $40-$45, and silent as you mentioned.

I just assume that the larger Z8 chassis might handle the heat a little better? The Z6 works for my use, but the CPUs are definitely running on the hot side.

Maybe. With a bunk front fan on my z820, everything still runs relatively cool at maybe a max of ~55C (2 CPUs, 3 GPUs, 16 sticks of RAM) according to sensors util in RHEL.

What'ya simulating?

Electric grid dynamic/transient event (disturbance) simulations. It's essentially modeling the response of electromechanical equipment (generators and large loads), large solid state devices (utility scale renewables and fast-switching reactive devices), lumped load models, and protective devices to events on the electric system. The simulations run in ~1ms timesteps over 10-30 seconds of real world time (which can take 4-20 minutes of CPU time). Each event is independent and they parallelize pretty much linearly. Python multiprocessing is the lifeblood of this work.

Edit: I guess I should be clear and say that the simulations themselves are running on a commercial software package (Fortran based) and I'm simply setting them up, spinning them off, and then processing all of the resulting data in Python.

Neat. I do a lot of RF simulations. Even though it's 60 Hz vs 60 GHz, I suppose a massive electrical grid has the same distributed effects that can cause standing waves and stability issues.

I use my z820 and z620 for running OpenShift/Kubernetes/Containers/VM workloads.