frozenport
A year ago it wasn't clear if they'd stay competitive but it seems they are.
There isn't to be shared between the two techs, Groq's hardware is a like a railgun that installs all the weights into the optimal location before firing off an inference. Cerebras computer engineering more convention requiring the same data movement that GPUs struggle with optimizing.
Suspect Groq is complementary/superior to nvidia's GPUs, while it is unclear what Cerebras brings other then maybe some deals with TSMC.
Typically running something like git would be an opt in permission.
Despite posturing by some academic administrators, most folks have no social agenda for a country they recently immigrated to.
So ideally the ml network would solve the problem end to end but these authors seem to be using a network for only one step of their otherwise classic image processing pipeline
Term sheets from vcs increasingly include a “don’t do an ico”
In the immediate it seems to have cut short the next generation of scientists leaving more of the entrenched old hands.
They read these stories of escape as emblematic of the southern government’s cowardice rather than heroism. In some ways these are stories of active military deserting their posts.
It was no surprise to these North Vietnamese patriots that they triumphed.
“exotic” signal representation without much practical utility despite the fact that they have been around the signal and image processing community for more than 30 years now.
Maybe they aren't that good? Maxwell's equations got a lot better when they dumped them, same thing with the few uses in video game physics/camera tracing.So if you're doing incremental stuff might as well stub out the calls with "not implemented", and start filling them in.
It seems like this is when you have the source or the libs but choose to mix x86 and arm?
It would seem if you have the source etc you should just bite the bullet and port everything.
So every clock cycle you're doing useful work rather than loading up people into batches. And thats why the arch will probably win for inference, for training you're basically competing with software eco system and silicon density. AKA NVIDIA can give TSMC more money to get more ALUs on the die.
I think other places have attempted dataflow (FPGA etc) but they all basically had buffers (due to non-determinism in networks stack and even ram). SambaNova seems indistinguishable from an FPGA with a few clock cycles difference. I think they blew their shot with a Series D ($600 million???) where they made more of the same old. Maybe Intel will buy them to augment Altera? Looks like chasing parity with existing strategies.
I buy the Groq hype because its something different, certainly the public demo helped. HN is about the future.
[1] https://www.semianalysis.com/p/groq-inference-tokenomics-spe...
Economics and costs are hard to predict. For example, Groq is not using HBM chips. So probably the cards are a lot easier to source.
Its not clear what the capacity of these systems are in terms of total users, or even tokens per second. Then you factor in cost. Then you realize all vendors will match a competitors pricing. Then you realize Groq doesn't sell chips.
¯\_(ツ)_/¯
The only thing you have is the public API to benchmark against: https://artificialanalysis.ai/
A war between the two countries caused the death
Also the bug was known, and more of a limitation that required the system to be power cycled (and this unit wasn't).