>He's running forty Blades in 2U. That's:
>
> 160 ARM cores
> 320 GB of RAM
> (up to) 320 terabytes of flash storage
>
>...in 2U of rackspace.Yay that's like... almost as much as normal 1U server can do
Edit: I give up, HN formatting is idiotic
The big win here would be that all of the network wiring is "built in" and compact. Blade replacement it trivial.
Have your fans blow up from the bottom and stagger "slots" on each row and if you do 32 slots per row, you probably build a kilocore cluster in a 6U box.
Ah the fun I would have with a lab with an nice budget.
I really like Graviton from AWS, and Apple Silicon is great, I really hope we move towards ARM64 more. ArchLinux has https://archlinuxarm.org , I would love to use these to build and test arm64 packages (without needing to use qemu hackery, awesome though that it is).
* 4 Ampere Altra Max processors (in 2 or 4 servers), so about 512 cores, and much faster than anything those Raspberry Pi have.
* lots of RAM, probably about 4TB ?
* ~92TB of flash storage (or more ?)
Edit : I didn't want to disparage the compute blade, it looks like a very fun project. It's not even the same use case as the server hardware (and probably the best solution if you need actual raspberry pis), the only common thread is the 2U and rack use.
More efficient use of space compared to my current silent mini-home lab -- also about 2U worth of space, but stacked semi-vertically [1].
That's 4 servers each with AMD 5950x, 128GB ECC, 2TB NVMe, 2x8TB SSD (64c/512GB/72TB total).
At this rate I have so little hope in other vendors that we'll probably just have to wait for the RPi5.
There is still near zero availability in mass market for CPUs you can stick into motherboards from one of the top ten taiwanese vendors of serious server class motherboards.
And don't even get me started on the lack of ability to actually buy raspberry pi of your desired configuration at a reasonable price and in stock to hit add to cart.
https://www.theverge.com/2014/6/4/5779468/twitter-engineer-b...
I'm noticing that our JVM workloads execute _significantly_ faster on ARM. Just looking at the execution times on our lowly first-gen M1s Macbooks is significantly better than some of our best Intel or AMD hardware we have racked. I'm guessing it all has to do with Memory bandwidth.
Will need to deal with NUMA issues on the software side.
They're not going to get maximum performance from a nvme disk, the cpus are too slow, and gigabit isn't going to cut it for high throughput applications.
Until manufacturers start shipping boards with ~32 cores clocked faster than 2ghz and multiple 10gbit connections, they're nothing more than a fun nerd toy.
I would, however, say that while I'm in the general target audience, I won't do crowdfunded hardware. If it isn't actually being produced, I won't buy it. The road between prototype and production is a long one for hardware.
(Still waiting for a very cool bit of hardware, 3+ years later - suspecting that project is just *dead*)
> 320 GB of RAM
Depending how you feel about hyperthreading, there are commodity dual-CPU Xeon setups than can do this as well.
Probably with one of these in the middle: http://www.mercotac.com/html/830.html
Then the next semester I am denied the ability to take a parallel computing class because it was for graduate students only and the prof. would not accept a waiver even though the class was being taught on the cluster me and a buddy built.
That I still had root on.
So I added a script that would renice the prof.'s jobs to be as slow as possible.
BOFH moment :)