I think the big news is that AMD is getting into memory-business so they won't be so dependent on Hynix and what have you. Memory is the bottleneck currently.
AMD doesn't even have their own fans.
AMD acquires Taalas to boost inference performance by etching models in silicon
451–460 of 712 posts
Re: AMD acquires Taalas to boost inference performance by etching models in silicon
#452so in the future I can buy KIMI, GLM or whatever model that get "soldered" directly into GPU ????
so instead of RTX xx70 series, I can buy xxTA that have kimi integrated ??? is that right ??
Re: AMD acquires Taalas to boost inference performance by etching models in silicon
#453Re: AMD acquires Taalas to boost inference performance by etching models in silicon
#454Earlier quoted context omitted.
For fast Kimi K3? You're damn right I do
$1,000 only gets you the Qwen 27B cartridge. For Kimi K3 it would be more like $100,000 (and the "cartridge" is the size of a refrigerator).
I would gladly pay $100,000 for local K3 running at 18,000 tok/sec.
Re: AMD acquires Taalas to boost inference performance by etching models in silicon
#455What I like about this, is that it significantly increases the probability of a sci-fi scenario where you're picking up a hot chip on the black market; rumor has it, Mythos 9 weights baked in...
steadily heading towards the final goal of computers being composed of pluggable crystals as stargate intended
Re: AMD acquires Taalas to boost inference performance by etching models in silicon
#456Earlier quoted context omitted.
I don’t think average user _needs_ to solve frontier challenges. ”Call to Jane”, ”turn on the lights” and ”what’s the weather this afternoon” is more like it I would guess. Ofc if the model has some critical bugs that’s another matter.
Your examples worked on phones for over a decade. Maybe baking in a model that is "certified" to have some unconditioned truths + rest is pulled from external models/store could make sense. But AFAIK that doesn't exist and I'm not sure it can possibly be made. Perhaps society as a whole at least can work on an open corpus of training data, but I'm not holding my breath on this.
>Your examples worked on phones for over a decade.
Nope. And not only not a decade ago, right now.
If you have an Android or iPhone, you can give it clear and easy to understand instructions that Gemma 4 could complete[1] if it had tool calls on it, and that 100.00% of Claude, ChatGPT, Grok, Kimi, you name it, could understand and all complete if they had the access.
The phones will fail to complete it. I just tried Siri. I said "hey Siri", waited for Siri to come up, and then I asked one of the exact sentences you replied to: "what's the weather this afternoon?" It thought for around 20 seconds, and said "Something went wrong. Please try again."[2]
I have Wifi, I have mobile Internet, I have free storage space, I have up to date software. What went wrong is that phones have never properly connected agents, not ten years ago, not last year, not this year, and probably not next year.
But don't settle for what Google could do in 1999 by hotlinking the keyword "weather" in any query to the weather being shown in the results.
Tell your phone (any phone): "Please call back the last number that called me that is not an unlisted number, regardless of who it came from."
0 out of any phone will complete that today, tomorrow, a year from now, five years from now, ever, because phone makers are not going to let them do that.
Meanwhile, 100% of all frontier agents could complete it if they had tool calls on the phone. Which they don't, and won't ever, thanks to the duopoly.
Okay, that's a bit dismissive, I would love to be wrong!
[1] after any voice recognition to text - which does work really well on both Android and iPhone! [2] screenshot: https://ibb.co/21rtDnfV
Re: AMD acquires Taalas to boost inference performance by etching models in silicon
#457I'm surprised neither OpenAI nor Anthropic made this move first. The Chinese open weight models are pulling ahead and commoditizing their value proposition. Baking models onto silicon would've been the next logical move to get a moat. Google is already doing this and has an experimental project on top of already having TPUs and cramming their quantized flash onto individual TPUs for inference.
We don’t really known (from the outside) how exactly do they move. Besides it may have not been truly viable 1-2 years ago…
Re: AMD acquires Taalas to boost inference performance by etching models in silicon
#458I'm surprised neither OpenAI nor Anthropic made this move first. The Chinese open weight models are pulling ahead and commoditizing their value proposition. Baking models onto silicon would've been the next logical move to get a moat. Google is already doing this and has an experimental project on top of already having TPUs and cramming their quantized flash onto individual TPUs for inference.
Chinese already start making DUV which can do the lower end 7nm. They are winning. Once that 7nm and up market cornered by Chinese, AMD Intel and TSMC and Samsung will have to burn thru bleeding edge depreciation faster perhaps from 7yr down to just 18mths. The CPU they generated will be incredibly expensive. Meanwhile Chinese just keep minting the AI cheaply and more efficiently and inching upwards towards 1.4nm.
They can do 7nm. But they use multiple patterning(printing the same pattern multiple times to get to 7nm), which is expensive. So it's not comparable on cost to western single-patterning 7nm, and of course not to the leading edge on cost/size/power.
Re: AMD acquires Taalas to boost inference performance by etching models in silicon
#459I feel like this will be the end of Taalas, AMD has for the most part of its history always chosen the wrong options.
Re: AMD acquires Taalas to boost inference performance by etching models in silicon
#460Earlier quoted context omitted.
The Taalas chips are not physically small. And part of their secret (if you look at the design) is just locating a bunch of memory soldered on the edges ( I belive higher amounts of SRAM ? )
Baking the base models on to ROM makes a lot of economic sense. SRAM for the KV cache & fine-tunes, not so much. Sure you’d get incredible speeds but it’s not scalable from a die-size or cost perspective. Rather base model on ROM + KV cache on DRAM is much more scalable. Also this would work great for edge devices that have a 2-5 year lifecycle.
I think the lifecycle for these chips could stretch far longer. If you're offering these models on a two year lifecycle, then you'd be able to stand up your top tier (wouldn't need to be frontier) at high speed. Run (for example) Kimi K3 on it and give it a brand name:
AcmeAI Carbon
Market it as your premier (only) model at high throughput. Two years later you stand up MSICs for the new state of the art with entirely new hardware, your lineup becomes:
AcmeAI Nitrogen (top tier) AcmeAI Carbon (mid tier)
If you just kept pushing the same model down your pricing tier over time you could still extract a lot of value from an old model, even years after it's been set in stone. Working on brand new code/frameworks? Pay to use the newest model. Working on legacy code? Use the lower tier models that will already know your legacy frameworks, pay far less and still get massive throughput. I've worked on a lot of government projects that this would be absolutely brilliant for.
The other side of this is that agent harnesses are NOT set in stone, so even a legacy model with a knowledge cut-off that's years out of date can likely still be helped quite a bit by harness and fetch behaviours that are still developing rapidly. Especially at this kind of throughput.