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.
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.
But yes, the less of a core building block the specific service is (or widely used internally in Amazon), the more likely you are to run into significant issues.
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?
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.
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.
The inf1/inf2 spot instances are so unpopular that they cost less than the equivalent cpu instances. Exact same (or better) hardware but 10-20% cheaper.
We're not quite seeing that on the trn1 instances yet, so someone is using them.
Heh, I was looking at an eks cluster recently that was using Cast AI autoscalar. Scratching my head as there was a bunch of inf instances. Then I realized it must be cheap spot pricing.
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.
With GCP announcing they built Gemini 3 on TPUs the opposite is true. Anthropic is under pressure to show they don’t need expensive GPUs. They’d be catching up at this point, not leaking some secret sauce. No reason for them to not boast on stage today unless there’s nothing to boast about.
Anthropic is not going to interrupt their competitors if their competitors don't want to use trainium. Neither would you, I, nor anyone else. The only potential is downside. There's no upside potential for them at all in doing so.
From Anthropic's perspective, if the rest of us can't figure out how to make trainium work? Good.
Amazon will fix the difficulty problem with time, but that's time Anthropic can use to press their advantages and entrench themselves in the market.
> 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.
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.
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 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.
But yes, the less of a core building block the specific service is (or widely used internally in Amazon), the more likely you are to run into significant issues.
I wonder if the difference is stuff they dogfood versus stuff they don't?
Enlight us...
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.
We're not quite seeing that on the trn1 instances yet, so someone is using them.
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
This is the AWS press release from last month saying Anthropic is using 500k Trainium chips and will use 500k more: https://finance.yahoo.com/news/amazon-says-anthropic-will-us...
And this is the Anthropic press release from last month saying they will use more Google TPUs but also are continuing to use Trainium (see the last 2 paragraphs specifically): https://www.anthropic.com/news/expanding-our-use-of-google-c...
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.
Anthropic is not going to interrupt their competitors if their competitors don't want to use trainium. Neither would you, I, nor anyone else. The only potential is downside. There's no upside potential for them at all in doing so.
From Anthropic's perspective, if the rest of us can't figure out how to make trainium work? Good.
Amazon will fix the difficulty problem with time, but that's time Anthropic can use to press their advantages and entrench themselves in the market.
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.
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.
https://newsletter.semianalysis.com/p/amazons-ai-self-suffic...
[0] https://www.godaddy.com/domainsearch/find?domainToCheck=trai...
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.
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.
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.
Pretty accurate in my experience, especially re: the neuron sdk. Do not use.