Amazon launches Trainium3
(techcrunch.com)176 points by thnaks 16 hours ago
176 points by thnaks 16 hours ago
IMO AWS once you get off the core services is full of beta services. S3, Dynamo, Lambda, ECS, etc are all solid. But there are a lot of services they have that have some big rough patches.
RDS, Route53, and Elasticache are decent, too. But yes, I've also been bitten badly in the distant past by attempting to rely on their higher-level services. I guess some things don't change.
I wonder if the difference is stuff they dogfood versus stuff they don't?
I once used one of their services (I forget which, but I think it was there serverless product) that “supported” Java.
… but the official command line tools had show-stopper bugs if you were deploying Java to this service, that’d been known for months, and some features couldn’t be used in Java, and the docs were only like 20% complete.
But this work-in-progress alpha (not even beta quality because it couldn’t plausibly be considered feature complete) counted as “supported” alongside other languages that were actually supported.
(This was a few years ago and this particular thing might be a lot better now, but it shows how little you can trust their marketing pages and GUI AWS dashboards)
Or when your AWS account rep is schmoozing your boss trying to persuade them to use something that is officially deprecated, lol.
Amazon Connect is a solid higher level offering. But only because it is a productized version of Amazon Retail’s call center
True with Cloudflare too. Just stick with Workers, R2, Durable Objects, etc...
Hmm is it actually that bad? Keep in mind r2 is only stored in one region which is chosen when the bucket is first created so that might be what you're seeing
But I've never really looked too closely because I just use it for non-latency critical blob storage
Agree, Google put a ton of work into making TPUs usable with the ecosystem. Given Amazon’s track record I can’t imagine they would ever do that.
AWS keeps making grand statements about Trainium but not a single customer comes on stage to say how amazing it is. Everyone I talked to that tries it says there were too many headaches and they moved on. AWS pushes it hard but “more price performant” isn’t a benefit if it’s a major PITA to deploy and run relative to other options. Chips without a quality developer experience isn’t gonna work.
Seems AWS is using this heavily internally, which makes sense, but not observing it getting traction outside that. Glad to see Amazon investing there though.
Not just AWS, looks like Anthropic uses it heavily as well. I assume they get plenty of handholding from Amazon though. I'm surprised any cloud provider does not invest drastically more into their SDK and tooling, nobody will use your cloud if they literally cannot.
Well AWS says Anthropic uses it but Anthropic isn’t exactly jumping up and down telling everyone how awesome it is, which tells you everything you need to know.
If Anthropic walked out on stage today and said how amazing it was and how they’re using it the announcement would have a lot more weight. Instead… crickets from Anthropic in the keynote
AWS has built 20 data centers in Indiana full of half a million Trainium chips explicitly for Anthropic. Anthropic is using them heavily. The same press announcement that Anthropic has made about Google TPUs is the exact same one they made a year ago about Trainium. Hell, even in the Google TPU press release they explicitly mention how they are still using Trainium as well.
I met a AWS engineer a couple of weeks ago and he said Trainium is actually being used for Anthropic model inference, not for training. Inferentia is basically defected Trainiums chips that nobody wants to use.
> Anthropic isn’t exactly jumping up and down telling everyone how awesome it is, which tells you everything you need to know.
You can’t really read into that. They are unlikely to let their competitors know if they have a slight performance/$ edge by going with AWS tech.
> I'm surprised any cloud provider does not invest drastically more into their SDK and tooling
I used to work for an AI startup. This is where Nvidia's moat is - the tens of thousands of man-hours that has gone into making the entire AI ecosystem work well with Nvidia hardware and not much else.
It's not that they haven't thought of this, it's just that they don't want to hire another 1k engineers to do it.
>I'm surprised any cloud provider does not invest drastically more into their SDK and tooling, nobody will use your cloud if they literally cannot.
Building an efficient compiler from high-level ML code to a TPU is actually quite a difficult software engineering feat, and it's not clear that Amazon has the kind of engineering talent needed to build something like that. Not like Google which have developed multiple compilers and language runtimes.
Heavens to Betsy, I don’t know if you can hear me, But try supporting these things if you actually want them to be successful. About the 3rd day into trying to roll your own LMI container in sagemaker because they haven’t updated the vLLM version in 6 months and you can’t run a regular sagemaker endpoint because of a ridiculous 60s timeout that was determined to be adequate 8 years ago. I can only imagine the hell that awaits the developer that decides to try their custom silicon.
Single-chip specs according to:
https://awsdocs-neuron.readthedocs-hosted.com/en/latest/abou...
https://awsdocs-neuron.readthedocs-hosted.com/en/latest/nki/...
Eight NeuronCore-v4 cores that collectively deliver:
2,517 MXFP8/MXFP4 TFLOPS
671 BF16/FP16/TF32 TFLOPS
2,517 FP16/BF16/TF32 sparse TFLOPS
183 FP32 TFLOPS
SRAM: 32 MiB * 8 = 256 MiB (ignoring 2 MiB * 8 = 16 MiB of PSUM which is not really general-purpose nor DMA-able)
Interconnect: 2560 GB/s (I think bidirectional, i.e. Jensen Math™)
----
At 3nm process node the FLOP/s is _way_ lower than competition. Compare to B200 which does 2250 BF16, x2 FP8, x4 FP4. TPU7x does 2307 BF16, x2 FP8 (no native FP4). HBM also lags behind (vs ~192 GiB in 6 stacks for both TPU7x and B200).
The main redeeming qualities seem to be: software-managed SRAM size (double of TPU7x; GPUs have L2 so not directly comparable) and on-paper raw interconnect BW (double of TPU7x and more than B200).
The Block floating point (MXFP8/4 stuff) is interesting; the AI stuff is really pushing basic data types that haven't moved for decades.
Anyone considering using trainium should view this Completely Factual Infomercial: https://x.com/typedfemale/status/1945912359027114310
Pretty accurate in my experience, especially re: the neuron sdk. Do not use.
yea, they "officially" dont release benchmarks even if we asked the AWS reps
Interesting that in the article, they do not say what the chip actually does. Not even once.
A bunch of 128x128 systolic arrays at its heart. More details:
https://newsletter.semianalysis.com/p/amazons-ai-self-suffic...
Nice article; I hate to think what the DC bus bars look like! ~50v at ~25kW/rack; 500A bus bars - I guess split, but still!
Ironically these chips are being targeted at inference as well (the AWS CEO acknowledged the difficulties in naming things during the announcement).
Time to go squat on trainium4.com [0]
[0] https://www.godaddy.com/domainsearch/find?domainToCheck=trai...
the real news is: "and teases an Nvidia-friendly roadmap"
The sole reason amazon is throwing any money at this is because they think they can do to AI what they did with logistics and shipping in an effort to slash costs leading into a recession (we cant fire anyone else.) The hubris is magnanimous to say the least.
but the total confidence is very low...so "Nvidia friendly" is face saving to ensure no bridges they currently cross for AWS profit get burned.
at some point the cost of transferring will dwarf the cost you pay to NVIDA. I bet that is their bet
Amazon aside, interesting future here with NVLink getting more and more folks using it. Intel is also onboard with NVlink. This is like an PCI -> AGP moment, but Nvidia's AGP.
AMD felt like they were so close to nabbing the accelerator future back in HyperTransport days. But the recent version Infinity Fabric is all internal.
There's Ultra Accelerator Link (UALink) getting some steam. Hypothetically CXL should be good for uses like this, using PCIe PHY but lower latency lighter weight; close to ram latency, not bad! But still a mere PCIe speed, not nearly enough, with PCIe 6.0 just barely emerging now. Ideally IMO we'd also see more chips come with integrated networking too: it was so amazing when Intel Xeon's had 100Gb Omni-Path for barely any price bump. UltraEthernet feels like it should be on core, gratis.
NVLink Fusion sounds like a total trap where you pay to become Jensen's slave. It may make sense for Intel because they're desperate. It's not a good look for AWS to put themselves in the same category.
UltraEthernet feels like it should be on core, gratis.
I've been saying for a while that AMD should put a SolarFlare NIC in their I/O die. They already have switchable PCIe/SATA ports, why not switchable PCIe/Ethernet? UEC might be too niche though.
I've had to repeatedly tell our AWS account reps that we're not even a little interested in the Trainium or Inferentia instances unless they have a provably reliable track record of working with the standard libraries we have to use like Transformers and PyTorch.
I know they claim they work, but that's only on their happy path with their very specific AMI's and the nightmare that is the neuron SDK. You try to do any real work with them and use your own dependencies and things tend to fall apart immediately.
It was just in the past couple years that it really became worthwhile to use TPU's if you're on GCP and that's only with the huge investment on Google's part into software support. I'm not going to sink hours and hours into beta testing AWS's software just to use their chips.