Earlier quoted context omitted.
I work in SRE and the way you describe it would give me pause. The first is that SRE team size primarily scales with the number of applications and level of support. It does scale with hardware but sublinearly, where number of applications usually scales super linearly. It takes a ton less effort to manage 100 instances of a single app than 1 instance of 100 separate apps (presuming SRE has any support responsibiliti…
Many people assume that companies need or want global enterprise level of management of infrastructure or 24/7 support. That's simply not the case. Many small and mid-sized companies just need their applications to run. There is no CTO on the board and nobody else really cares where the stuff runs if it fits a certain budget, is available enough to not cause major disruptions and is responsive enough to not cause com…
Intel's make-or-break 18A process node debuts for data center with 288-core Xeon
291–300 of 303 posts
Re: Intel's make-or-break 18A process node debuts for data center with 288-core Xeon
#292Re: Intel's make-or-break 18A process node debuts for data center with 288-core Xeon
#293Re: Intel's make-or-break 18A process node debuts for data center with 288-core Xeon
#294Earlier quoted context omitted.
Bus factor 1 is rarely enough for "entire business". But if the GPUs are for training models, and their users are the data scientists that are also on holiday around the same times - that might indeed be good enough policy.
> and their users are the data scientists that are also on holiday around the same times I’ve seen this before. It turns into restrictions on when you can schedule vacation times. Not fun when your family wants to go on a trip but you can’t get the time off because it’s not one of the allowed vacation times.
Re: Intel's make-or-break 18A process node debuts for data center with 288-core Xeon
#295Earlier quoted context omitted.
No it's actual hardware coherent memory across the system. At a high level it is the same way two cores/caches are connected within one chip, or the same way two sockets are connected on the same board. Just using cables instead of wires in the chip or on a board. This system has SMP ASICs on the motherboards that talk to a couple of Intel processor sockets using their coherency protocol over QPI and they basically p…
Thanks for answering. So it's not CXL, instead it's proprietary ASICs masquerading as NUMA nodes but actually forwarding to their counterparts in the other chassis? Are they proprietary to HP or is this some new standard?
Re: Intel's make-or-break 18A process node debuts for data center with 288-core Xeon
#296Earlier quoted context omitted.
A very small model is going to be, what, 8GB? That'll easily blow through the caches. You're going to end up bottlenecked on DRAM either way. So, I wonder if this is going to be any faster than the previous generation for edge AI.
Perhaps instead of posting erroneous assertions to HN you could wander over to your LLM of choice and ask it something along the lines of: What are some examples of edge AI applications that achieve good performance on a CPU where memory bandwidth is severely limited compared to a GPU? Please link to publicly available models where possible.
None of those fit in 4MB of cache (the per-core on this part), or 1GB (the aggregate cache).
What AI models are you actually talking about? Do you mean old-school ML stuff, like decision trees or high dimensional indexes? No one I know calls those "AI", which is generally reserved for big-ish neural networks.
Re: Intel's make-or-break 18A process node debuts for data center with 288-core Xeon
#297Earlier quoted context omitted.
Perhaps instead of posting erroneous assertions to HN you could wander over to your LLM of choice and ask it something along the lines of: What are some examples of edge AI applications that achieve good performance on a CPU where memory bandwidth is severely limited compared to a GPU? Please link to publicly available models where possible.
I run AI applications all the time in exactly those situations. The models range from 2GB (vector models) 30GB (small LLMs) to 100GB (medium LLMs). None of those fit in 4MB of cache (the per-core on this part), or 1GB (the aggregate cache). What AI models are you actually talking about? Do you mean old-school ML stuff, like decision trees or high dimensional indexes? No one I know calls those "AI", which is generally…
I let you know that your were uninformed and even suggested a very low effort way that you might look into the matter. So why didn't you do that?
A couple fairly arbitrary examples. A high performance zero shot TTS model can weigh in at well under 150 MiB. You can solve MNIST (ie perform OCR of handwritten english) to better than 99% accuracy with a sub-100 KiB model. Your LLM of choice will be able to provide you with plenty of others.
Re: Intel's make-or-break 18A process node debuts for data center with 288-core Xeon
#298Earlier quoted context omitted.
Agree entirely with your take. The packaging story is awesome, I wish there were more details on the stacking used on this one. But I am at a loss to how Intel are really going to get any traction with IFS. How can anyone trust Intel as a long-term foundry partner. Even if they priced it more aggressively, the opportunity cost in picking a supplier who decides to quit next year would be catastrophic for many. The onl…
> How can anyone trust Intel as a long-term foundry partner With the standard form of business trust: a contract.
Re: Intel's make-or-break 18A process node debuts for data center with 288-core Xeon
#299Earlier quoted context omitted.
> How can anyone trust Intel as a long-term foundry partner With the standard form of business trust: a contract.
Worthless. Just looks how IFS worked out the previous two times they gave it a go. If you're not in the industry you may not even be aware it was a thing. And then not. Twice.
Re: Intel's make-or-break 18A process node debuts for data center with 288-core Xeon
#300These sorts of core-density increases are how I win cloud debates in an org. * Identify the workloads that haven't scaled in a year. Your ERPs, your HRIS, your dev/stage/test environments, DBs, Microsoft estate, core infrastructure, etc. (EDIT, from zbentley: also identify any cross-system processing where data will transfer from the cloud back to your private estate to be excluded, so you don't get murdered with egr…
Context: been helpdesk, sysadmin & network admin, DevOps, Site Reliability Engineer, in that progression, starting in the 90's. Max on-prem was 40 racks, scaled up and down over years.
Many comments talking about how staffing is a key element of this equation that can't be overlooked, but I decided to reply to the root comment, which doesn't say whether/how it considers staffing.
This is a complex equation - and it is relatively easy to present an incomplete or misleading picture management to push the move into the cloud.. or out of the cloud.
Some factors, in no particular order:
1) Scaling: it is self-evident that pulling a single physical server worth out of the cloud is not worth it.. even for 288 cores. Or perhaps 1152 for 4xXeon in a single server. Still likely not worth it. Why? Because a single server is never just that. Someone has to swap components when it goes down. When it goes down.. ALL 1152 cores are down, along with everything they are doing. Is that acceptable for all applications running on all those cores? It is also appropriate supporting infrastructure - power, cooling, physical space. The "fairly obvious" minimum scaling is "enough servers that one can be entirely down for maintenance while keeping everything else running." But now you're paying for some overhead. At 2 servers, you're buying 2x what you need, half that capacity is idle all the time. And so on.
On this point - I think the other comments talking about "each SRE managing 4 (or 5, or 7)" racks missed the point entirely. SRE's should be doing scalable work, whether in the cloud, or on-prem. And they should NOT be swapping failed hard drives and power supplies. Designing a larger-than-one rack install is probably worth hiring consultants for if you don't have that expertise in-house, though the SREs that would be supporting it would need to supply lots of input. To some extent, server & network equipment vendors can also help. It is not trivial as the scale goes up. But then it should run for some years, with relatively unskilled people handling hardware failures and you can re-engage consultants if necessary to do upgrades as hardware and needs evolve.
But your SREs should be on-staff, and probably on-call to handle the software running on that hardware.. and to some extent to call the remote hands to deal with hardware failures.
2) Business needs: does the business need the tech skills that self-hosting requires for the core business? For example - if the business itself is cloud SAAS, maybe DIY-ing at least some of your infrastructure is right in your wheelhouse. If so - a modest increase in staff could mean a huge cost savings. But if not, all the cost of skilled staff to run it is simply part of the cost of in-housing this stuff.
3) Staffing: the people that swap broken hardware are not the same people that respond to pages because the business critical application crashed due to a bug. You can pay a colo facility for all this, typically by the hour - but it isn't cheap and you've got to supply all the spares etc. Is that part of your budget for on-prem?
4) On-call: maybe your self-hosted ERP system can be down every night and weekend without issues.. and even business hours can tolerate 98% uptime. But that doesn't mean you can get away without having someone on call - presumably you're hosting more than just this lowish-requirement ERP system. I'll disagree with other comments - the "no burnout" number of on-call staff you need is 6-7, not 4! Remember people take vacations too. This is well studied, and established, I'll reference Tom Limoncelli's books. This could be relatively cheap and require fewer staff with geo-distributed staff, and it would tend to overlap with staff you already use to provide on-call for anything you host on the cloud - so maybe for your situation it is close to a wash. But you can't forget to budget for it even if the line item is $0.
5) Vendor support: maybe you already have your own data center or colo and are hosting a ton of stuff. Why not move all your Atlassian stuff in house and save the hosting cost. Oh.. wups, Atlassian simply doesn't support that any more. Host it with Atlassian or GTFO. A minor point as most vendors would give you enough notice you can simply run out the lifetime of the hardware it is on and not replace it.
6) Market pricing: At one point Amazon was starting "by the minute, by the core cloud" (as opposed the older "cloudish" model of leasing only an entire physical server by the entire year) and priced a bit under market to get going. Then once they established dominance, they cranked the price WAY up for profit extraction. But now they do have some competition and they're a bit more selective about how they extract profits. In my perception they've shifted a lot of the profit taking to the value-added services rather than raw instance time, but I could be wrong. And they have HUGE costs - it is beyond naive to look at the per-hour cost of an instance and compare it to purchasing an identical physical server solely on the purchase price of that server. Corey Quinn / Duckbill group has spent a huge part of his career in this space - if you're already in the cloud 100% it is well worth optimizing those costs before you start comparing it to what on-prem might cost.