Posted by vanburen 5 hours ago
But right pricing hardware is hard if you’re small shop. My mind is hard-locked onto Epyc processors without thought. 9755 on eBay is cheap as balls. Infinity cores!
Problem with hardware is lead time etc. cloud can spin up immediately. Great for experimentation. Organizationally useful. If your teams have to go through IT to provision machine and IT have to go through finance so that spend is reliable, everybody slows down too much. You can’t just spin up next product.
But if you’re small shop having some Kubernetes on rack is maybe $15k one time and $1.2k on going per month. Very cheap and you get lots and lots of compute!
Previously skillset was required. These days you plug Ethernet port, turn on Claude Code dangerously skip permissions “write a bash script that is idempotent that configures my Mikrotik CCR, it’s on IP $x on interface $y”. Hotspot on. Cold air blowing on face from overhead coolers. 5 minutes later run script without looking. Everything comes up.
Still, foolish to do on prem by default perhaps (now that I think about it): if you have cloud egress you’re dead, compliance story requires interconnect to be well designed. More complicated than just basics. You need to know a little before it makes sense.
Feel like reasoning LLM. I now have opposite position.
Last time I tried to do anything networking with Claude it set up route preference in opposite order (it thought lower number means more preferred, while it was opposite), fucking it up completely, and then invented config commands that do not exist in BIRD (routing software suite).
Then I looked at 2 different AIs and they both hallucinated same BIRD config commands that were nonexistent. And by same I mean they hallucinated existence of same feature.
> If your teams have to go through IT to provision machine and IT have to go through finance so that spend is reliable, everybody slows down too much. You can’t just spin up next product.
The time of having to order a bunch of servers for new project is long over. We just spun k8s cluster for devs to self-service themselves and the prod clusters just have a bit of accounting shim so adding new namespace have to be assigned to a certain project so we can bill client for it.
Also you're allowed to use cloud services while you have on-prem infrastructure. You get best of both, with some cognition cost involved.
What are the dimensions and dynamics here vs EPYC?
Putting more cores is just another desperate move to play the benchmark. Power is roughly quadratic with frequency, every time you fall behind competition, you can double the number of cores and reduce the frequency by 1.414 to compensate.
Repeat a few times and you get CPU with hundreds of cores, but each core is so slow it can hardly do any work.
The Panther Lake vs Ryzen laptop performance comparisons show that Pather Lake does well, basically trading against top end Ryzen AI laptop chips in both absolute performance, and performance per watt.
GPU and CPU manufacturing is the same thing, same node, same result. GPU is always maximizing perf/power ratio because it's embarrassingly parallel, leaving no room to game the benchmark. CPU can be gamed by having a single fast core, that drops performance in half as soon as you use another core.
Getting the performance to scale can be hard, of course. The less inter-core communication the better. Things that tend to work well are either stuff where a bunch of data comes in and a single thread works on it for a significant amount of time then ships the result or things where you can rely on the NIC(s) to split traffic and you can process the network queue for a connecrion on the same core that handles the userspace stuff (see Receive Side Scaling), but you need a fancy NIC to have 288 network queues.
https://arstechnica.com/gadgets/2024/09/hacker-boots-linux-o...
As I understand things, it would be extremely unusual to ship a chip that was bound by floating point throughput, not uncached memory access, especially in the desktop/laptop space.
I haven't been following the Intel server space too carefully, so it's an honest question: Was the old thing compute and not bandwidth limited, or is this going to be running inference at the same throughput (though maybe with lower power consumption)?
Here is the quote:
"The company says operators deploying 5G Advanced and future 6G networks increasingly rely on server CPUs for virtualized RAN and edge AI inference, as they do not want to re-architect their data centers in a bid to accommodate AI accelerators."
Edge AI usually means very small models that run fine on CPUs.
So, I wonder if this is going to be any faster than the previous generation for edge AI.