I'm not sure if that's what you were going for, but I read it as if it were written by The Board in the game Control, and found myself with the appropriate level of existential dread.
Because there's been nothing to discuss since their announcement. Their API access immediately closed due to overwhelming demand and they didn't fab newer models than Llama3 yet.
Probably they will make bank selling to HFT for a while.
Funnily enough, pasting your comment straight into Jimmy leads to a... Funnily suboptimal answer that does not answer the question.
As someone else already contributed, this is driven by a Canadian startup taalas that basically makes chips that are llms, so everything is very fast but also, baked into the chip. Once this kind of stuff is a commodity in like 10 years, our world will be very, very different.
Taalas HC1 AI uses Llama 3.1 8B, but takes up a massive 53B transistors and 815mm2 on TSMC N6 (nearly at the reticle limit of 858mm2). N2 is a little less than 3x as dense (110MTr/mm2 vs 313MTr/mm2).
This chip would still be 272mm2 on N2 which is an eye-watering $30k/wafer and bigger than a 9950x or Nvidia 5070.
This just isn't feasible. Some of the latest-gen LLMs seem to have 5-10T parameters or about 1000x more. I don't know that taping out just one chip makes economic sense let alone the 300-1000 chips required for a cutting-edge model. Things like continuing education so your model knows about the latest NPM packages or world news is super important, but seems like it would require new chips.
There are a TON of uses for an 8B parameter models on the edge, but this is WAY too big to put on the edge of anything. Something like a 10mm2 100m parameter voice model might be feasible on the edge, but only for expensive devices, but most of those are TSMC 28nm (up to 29MTr/mm2) or GF FDX22 (up to 40MTR/mm2) which would increase the AI chip to the point where it would absolutely dominate the BOM.
This is genuinely confusing to my senses. The future is going to be so strange/neat/me unemployed.
> strange/neat/me unemployed
I'm not sure if that's what you were going for, but I read it as if it were written by The Board in the game Control, and found myself with the appropriate level of existential dread.
and I haven't played that game, so I read it in Ralph Wiggum's voice.. which also feels appropriate.
I'm in danger.
We love/help/replace you
The future is totally illegible to me. I love these AI models, but I feel like I'm going to be jobless within 10 years.
Anomie is at an all time high right now.
10 years? An optimist, I see.
Yeah. It keeps catching me off guard that it answered me already.
Why is the insane speed of 13KTPS of this site is not more on the the top of the AI conversations?
Because there's been nothing to discuss since their announcement. Their API access immediately closed due to overwhelming demand and they didn't fab newer models than Llama3 yet.
Probably they will make bank selling to HFT for a while.
Because I just tested it and it took 3-4 clarifications before it actually gave a correct response vs gemini/google search. It's not great, but good.
I'd rather wait 3x as long.
It's pretty well known by now.
I asked it for a block of C++ code and it hit 14,189 tok/s. I assume it cached someone else's session?
No - it's custom silicon https://news.ycombinator.com/item?id=48693490
Wow.. what?! How is this so fast?! Where can I read more?
Funnily enough, pasting your comment straight into Jimmy leads to a... Funnily suboptimal answer that does not answer the question.
As someone else already contributed, this is driven by a Canadian startup taalas that basically makes chips that are llms, so everything is very fast but also, baked into the chip. Once this kind of stuff is a commodity in like 10 years, our world will be very, very different.
Taalas HC1 AI uses Llama 3.1 8B, but takes up a massive 53B transistors and 815mm2 on TSMC N6 (nearly at the reticle limit of 858mm2). N2 is a little less than 3x as dense (110MTr/mm2 vs 313MTr/mm2).
This chip would still be 272mm2 on N2 which is an eye-watering $30k/wafer and bigger than a 9950x or Nvidia 5070.
This just isn't feasible. Some of the latest-gen LLMs seem to have 5-10T parameters or about 1000x more. I don't know that taping out just one chip makes economic sense let alone the 300-1000 chips required for a cutting-edge model. Things like continuing education so your model knows about the latest NPM packages or world news is super important, but seems like it would require new chips.
There are a TON of uses for an 8B parameter models on the edge, but this is WAY too big to put on the edge of anything. Something like a 10mm2 100m parameter voice model might be feasible on the edge, but only for expensive devices, but most of those are TSMC 28nm (up to 29MTr/mm2) or GF FDX22 (up to 40MTR/mm2) which would increase the AI chip to the point where it would absolutely dominate the BOM.
9 replies →
https://taalas.com/
Taalas https://news.ycombinator.com/item?id=47103661
This caused me to have some sense what blistering fast AI actually is. What it means for the future is a question that remains.
Damn that is crazy.
This is the reaction every time it's posted, and deservedly so.
Not opening here... HN killed?
What
How?
Which model is behind it?
It’s pure silicon. Llama3.
What does that even mean? There must be a software stack somewhere feeding the inputs to the model. Do you mean the weights are baked in sillicon?
hugged to death?
[dead]