To be clear, it was a CPU fault that doesn't occur at all when running e.g. stress-ng, but only (as far as I know) when running our particular production workload.

And only after several hours of running our production workload.

But then, once it's known to be provokeable for a given machine, it's extremely reliable to trigger it again — in that it seems to take the same number of executed instructions that utilize the faulty part of the die, since power on. (I.e. if I run a workload that's 50% AES-NI and 50% something else, then it takes exactly twice as long to fault as if the workload was 100% AES-NI.)

And it isn't provoked any more quickly, by having just provoked it and then running the same workload again — i.e. there's no temporal locality to it. Which would make both "environmental conditions" and "CPU is overheating / overvolting" much less likely as contributing factors.

> There have been enough of them in private hands for long enough that if there were widespread issues they would be well-known.

Our setup is likely a bit unusual. These machines that experienced the faults, have every available PCIe lane (other than the few given to the NIC) dedicated to NVMe; where we've got the NVMe sticks stuck together in extremely-wide software RAID0 (meaning that every disk read fans in as many almost-precisely-parallel PCIe packets contending for bus time to DMA their way back into the kernel BIO buffers.) On top of this, we then have every core saturated with parallel CPU-bottlenecked activity, with a heavy focus on these AES-NI instructions; and a high level of rapid allocation/dellocation of multi-GB per-client working arenas, contending against a very large and very hot disk page cache, for a working set that's far, far larger than memory.

I'll put it like this: some of these machines are "real-time OLAP" DB (Postgres) servers. And under load, our PG transactions sit in WAIT_LWLOCK waiting to start up, because they're actually (according to our profiling) contending over acquiring the global in-memory pg_locks table in order to write their per-table READ_SHARED locks there (in turn because they're dealing with wide joins across N tables in M schemas where each table has hundreds of partitions and the query is an aggregate so no constraint-exclusion can be used. Our pg_locks Prometheus metrics look crazy.) Imagine the TLB havoc going on, as those forked-off heavy-workload query workers also all fight to memory-map the same huge set of backing table heap files.

It's to the point that if we don't either terminate our long-lived client connections (even when not idle), or restart our PG servers at least once a month, we actually see per-backend resource leaks that eventually cause PG to get OOMed!

The machines that aren't DB servers, meanwhile — but are still set up the same on an OS level — are blockchain nodes, running https://github.com/ledgerwatch/erigon, which likes to do its syncing work in big batches: download N blocks, then execute N blocks, then index N blocks. The part that reliably causes the faults is "hashing N blocks", for sufficiently large values of N that you only ever really hit during a backfill sync, not live sync.

In neither case would I expect many others to have hit on just the right combination of load to end up with the same problems.

(Which is why I don't really believe that whatever problem AMD might have seen, is related to this one. This seems more like a single-batch production error than anything, where OVH happened to acquire multiple CPUs from that single batch.)

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> It's possible that AMD didn't order enough capacity from TSMC to meet demand, and couldn't get more during the COVID supply chain issues.

Yes, but that doesn't explain why they weren't able to ramp up production at any point in the last four years. Even now, there are still likely some smaller hosts that would like to buy EPYC 7xxxs at more-affordable prices, if AMD would make them.

You need an additional factor to explain this lack of ramp-up post-COVID; and to explain why the cloud providers never started receiving more 7xxxs (which they would normally do, to satisfy legacy clients who want to replicate their exact setup across more AZs/regions.) Server CPUs don't normally have 2-year purchase commitments! It's normally more like 6!

Sure, maybe Zen4c was super-marketable to the clouds' customers and saved them a bunch of OpEx — so they negotiated with AMD to drop all their existing spend commitments on 7xxx parts purchases in favor of committing to 9xxx parts purchases.

But why would AMD agree to that, without anything the clouds could hold over their head to force them into it? It would mean shutting down many of the 7xxx production lines early, translating to the CapEx for those production lines not getting paid off! Being able to pay off the production lines is why CPU vendors negotiate these long purchase commitments in the first place!

And if the clouds are replacing capacity, then where are all those used CPUs going?

Take notice that the OP article isn't talking about a used CPU, but a "new server" — namely (I think) this one: https://www.newegg.com/tyan-s8030gm4ne-2t-supports-amd-epyc-...

This server was never in an IaaS datacenter. This is a motherboard straight from the motherboard vendor, with an EPYC 7C13 prepopulated into it.

This isn't the sort of thing you get when a cloud resells. This is the sort of thing you get when a cloud (or other hosting provider) stops buying unexpectedly — and upstream suppliers/manufacturers/integrators are left holding the bag, of preconfigured-to-spec hardware they no longer have a pre-committed buyer for.