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.
I wonder if the difference is stuff they dogfood versus stuff they don't?
… 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)
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.
Enlight us...
Glue Catalog has some annoying assumptions baked in.
Frankly the entire analytics space on AWS feels like a huge mess of competing teams and products instead of a uniform vision.
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
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 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://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™)
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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).
Pretty accurate in my experience, especially re: the neuron sdk. Do not use.
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.
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.