Conclusion & End Remarks

The server landscape is changing very quickly. While the promise of Arm servers for many years has been just that – this year’s introduction of the Graviton2 marked the tipping point where Arm server chips no longer represented a niche use-case, but rather a real – and competitive option. The only problem with Graviton2 was that this was an internal Amazon-only solution – so you couldn’t really say it was an option against AMD or Intel.

That’s where Ampere Computing steps in, positioning themselves as an open merchant silicon vendor, and the first to use and deploy Arm’s new Neoverse CPU line-up in such a way. The Altra QuickSilver being the very first attempt at this, truly hits it out of the park and matches the high expectations of the silicon.

Ampere’s approach is significantly more aggressive, with more performance, and more power, than what the Graviton2 aimed for – the new 80-core Q80-33 flagship SKU essentially has managed to match the performance of AMD’s flagship Rome chip – the 64-core EPYC 7742. While personally that didn’t surprise me much, I could imagine that for many readers out there this to come as an unexpected turn of events.

The Altra Q80-33 sometimes beats the EPYC 7742, and loses out sometimes – depending the workload. The Altra’s strengths lie in compute-bound workloads where having 25% more cores is an advantage. The Neoverse-N1 cores clocked at 3.3GHz can more than match the per-core performance of Zen2 inside the EPYC CPUs.

There are still workloads in which the Altra doesn’t do as well – anything that puts higher cache pressure on the cores will heavily favours the EPYC as while 1MB per core L2 is nice to have, 32MB of L3 shared amongst 80 cores isn’t very much cache to go around. Generally, I think the mesh interconnect remains a weak-point for this generation of Neoverse products and there’s improvements to be done in the next iteration of designs.

Today we’ve tested the Wiwynn based “Mount Jade” 2S Ampere Altra server – the Altra’s support for dual-socket platforms is functional, but relying on CCIX instead of a native coherency protocol between CPU cores in the two sockets means that performance isn’t nearly as good as the scaling we see from AMD or Intel. The single-socket “Mount Snow” Altra platforms as well as the platform solutions from GIGABYTE might be a better option for some deployments.

In terms of power-efficiency, the Q80-33 really operates at the far end of the frequency/voltage curves at 3.3GHz. While the TDP of 250W really isn’t comparable to the figures of AMD and Intel are publishing, as average power consumption of the Altra in many workloads is well below that figure – ranging from 180 to 220W – let’s say a 200W median across a variety of workloads, with few workloads actually hitting that peak 250W. I would say that yes, the Altra does have a power efficiency advantage over AMD’s EPYC platform, but not something that is overly significant enough to say that it completely changes the landscape.

Ampere 1st Gen Altra 'QuickSilver'
Product List
AnandTech Cores Frequency TDP PCIe DDR4 Price
Q80-33
(Tested)
80 3.3 GHz 250 W 128x G4 8 x 3200 $4050
Q80-30 80 3.0 GHz 210 W 128x G4 8 x 3200 $3950
Q80-26 80 2.6 GHz 175 W 128x G4 8 x 3200 $3810
Q80-23 80 2.3 GHz 150 W 128x G4 8 x 3200 $3700
Q72-30 72 3.0 GHz 195 W 128x G4 8 x 3200 $3590
Q64-33 64 3.3 GHz 220 W 128x G4 8 x 3200 $3810
Q64-30 64 3.0 GHz 180 W 128x G4 8 x 3200 $3480
Q64-26 64 2.6 GHz 125 W 128x G4 8 x 3200 $3260
Q64-24 64 2.4 GHz 95 W 128x G4 8 x 3200 $3090
Q48-22 48 2.2 GHz 85 W 128x G4 8 x 3200 $2200
Q32-17 32 1.7 GHz 45 W 128x G4 8 x 3200 $800

Where Ampere and the Altra definitely is beating AMD in is TCO, or total cost of ownership. Taking the flagship models as comparison points – the Q80-33 costs only $4050 which generally matching the performance of AMD’s EPYC 7742 which still comes in at $6950, essentially 42% cheaper. Of course, performance/$ will vary depending on workloads, but the Altra’s performance is so good that I don’t think it really changes the narrative of that large a cost difference. We’re really on basing this on both companies’ MSRP prices and we know for a fact many customers will be paying less than that for volume purchases and relying on discounts, but that can also apply to Ampere and the Altra.

One will note I didn’t make any mention of Intel yet - Intel’s current Xeon offering simply isn’t competitive in any way or form at this moment in time. Cascade Lake is twice as slow and half as efficient – so unless Intel is giving away the chips at a fraction of a price, they really make no sense. Ice Lake-SP is around the corner, but I don’t expect it to manage to bridge the performance or efficiency gap. Ampere and AMD here have free reign on the server market share – with Ampere having to cross the hurdle to convince customers to switch over from x86 to Arm.

Ampere is already shipping Altra systems to customers, with Oracle’s cloud business being the first big notable win for the company – signifying already very positive reactions in the market.

What we need to keep in mind though, is that today’s comparisons were against AMD’s EPYC 7742 which was launched almost 15 months ago. Rome’s successor, Milan, is already shipping to customers and has already started hitting the channel, and we expect to hear more about the Zen3-based EPYC chips in the coming weeks. I’m not expecting major leaps, but a 20% performance bump is pretty much a safe bet to make – it would beat the Q80-33 in more workloads and shift the balance a bit – but Ampere’s aggressive pricing would still be something for AMD to worry about.

What really excites me, is the potential of future Altra designs. Ampere has already announced that Altra-Max “Mystique” will be coming in 2021 – essentially a 128-core version of the same Neoverse-N1 platform used in the QuickSilver design today. We’ll have to see how that scales, but it’ll certainly be a compute monster. The real big deal will be the 5nm 2022 “Siryn” design – if Ampere adopts the Neoverse-V1 CPU core from Arm, and I hope they will, then that would signify at minimum  a +50% performance uplift, which is massive.

The Altra overall is an astounding achievement – the company has managed to meet, and maybe even surpass all expectations out of this first-generation design. With one fell swoop Ampere managed to position itself as a top competitor in the server CPU market. The Arm server dream is no longer a dream, it’s here today, and it’s real.

Compiling LLVM, NAMD Performance
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  • mode_13h - Thursday, December 31, 2020 - link

    Isn't Blender included in SPECfp2017 as 526.blender_r? Or is that something different?
  • Teckk - Friday, December 18, 2020 - link

    Whoever decided on naming these products — fantastic job. Simple, clear and effective.
    Maybe you can offer some free advice to Intel and Sony.
  • Calin - Friday, December 18, 2020 - link

    The answer to the question of "how powerful it is" is clear - more than good enough.
    The real question in fact is:
    "How much can they produce?"
    AMD has the crown in x86 processor performance, but this doesn't really matter very much as long as they can build enough processors only for a part of the market.
  • jwittich - Friday, December 18, 2020 - link

    How many do you need? :)
  • Bigos - Friday, December 18, 2020 - link

    64kB pages might significantly enhance performance on workload with large memory sets, as the TLB will be up to 16x less used. On the other hand, memory usage of the Linux file system cache will also increase a lot.

    Would you be able to test the effect of 64kB vs 4kB page size on at least some workloads?
  • Andrei Frumusanu - Friday, December 18, 2020 - link

    It's something that I wanted to test but it requires a OS reinstall / kernel recompile - I didn't want to get into that rabbit hole of a time sink as already spent a lot of time verifying a lot of data across the three platforms over a few weeks already.
  • arnd - Friday, December 18, 2020 - link

    I'd love to see that as well. For workloads that use transparent huge pages, there should not be much difference since both would use 2MB huge pages (512*4KB or 32*64KB), plus one or more even larger page sizes, but it needs to be measured to be

    The downsides of 64KB requiring larger disk I/O and more RAM are often harder to quantify, as most benchmarks try to avoid the interesting cases.

    I've tried benchmarking kernel compiles on Graviton2 both ways and found 64kB pages to be a few percent faster when there is enough RAM, but forcing the system to swap by limiting the total RAM made the 64kB kernel easily 10x to 1000x slower than the 4kB one, depending on the how the available memory lined up with the working set.
  • abufrejoval - Friday, December 18, 2020 - link

    Thank you for the incredible amount of information and the work you put into this: Anandtech's best!

    Yet I wonder who would deploy this and where. The purchasing price of the CPU would seem to become a rather miniscule part of the total system cost, especially once you go into big RAM territory. And I wonder if it's not similar with the energy budget: I see my larger systems requiring more $ and Watts on RAM than on the CPUs. Are they doing, can they do anything there to reduce DRAM energy consumption vs. Intel/AMD?

    The cost of the ecosystem change to ARM may be less relevant once you have the scale to pay for it, but where exactly would those scale benefits come from? And what scales are we talking about? Would you need 100k or 1m servers to break even?

    And what sort of system load would you have to reach/maintain to have significant energy advantages vs. x86 iron?

    Do they support special tricks like powering down quadrants and RAM banks for load management, do they enable quick standby/actvation modes so that servers can be take off and on for load management?

    And how long would the benefits last? AMD has demonstrated rather well, that the ability to execute over at least three generations of hardware are required to shift attention even from the big guys and they have still all the scaling benefits the x86 installed base provides.

    These guys are on a 2nd generation product, promise 3rd but essentially this would seem to have the same level of confidence as the 1st EPIC.
  • askar - Friday, December 18, 2020 - link

    Would you mind testing ML performance, i.e. python's SKLearn library classes that can be multithreaded (random forest for example)?
  • mode_13h - Sunday, December 20, 2020 - link

    MLPerf?

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