Custom AI for things like facial recognition in cameras has existed for decades, before LLMs were a thing. I don't see that getting replaced. And on-device conversational intelligence might go that route as well, we'll have to wait and see. It's a lot of silicon to dedicated to a static non-changing thing. My money would be on programmable TPU-like things (Apple's NPU kind of stuff). It just seems more flexible to have an array of compute that you can load different models into, so you can update it, etc.
What about all the things you don't currently use an LLM for?
If a specialized chip can run a model 100 times faster, you can suddenly use it for a lot of things at sub-second latency. You can write "make white transparent and add a red outline to x.png" instead of the corresponding imagemagick invocation and perceive little to no latency difference. You can hook it up to your browser and have it yank out all advertisements live, or tell it to highlight anything that might interest you, again, live. There's probably thousands of latent use cases nobody has thought of that would be enabled by a truly fast LLM, even a mediocre one.
I don't think an on-device model needs to change much; it's already quite general in its capabilities.
Looking for a lamp in a specific style?
"Make a VR application set inside my apartment (based on all the photos of my apartment from my photos directory) with all lamps under $100 that fit Scandinavian interiors and could be delivered to my house before Friday. Place the lamp on the dining room table, allow us to: cycle through lamps, change time of day, and interact with all the lamps and furniture".
Five seconds later and bam you have this new piece of software that you'll use once and then dispose of.
AI: "I'm sorry, a security guardrail prevents me from performing this operation."
Your company will decide what makes economical sense.
If you can have only one AI processor in your laptop (because they're big and expensive), it's going to be a GPU. This AI processor needs to inference LLMs, audio processing, image generation, video generation, etc. This is on top of normal graphics processing requirements such as video games, playing videos, decoding, encoding, etc.
At the enterprise level, I can see some ASICs working once the market fully matures and improvements in architectures slow down drastically while demand for inference increases drastically. How far are we from this world? Maybe 5-10 years? It seems like model architectures are still changing rapidly and labs want fast experimentation that programmable GPUs offer.
GPUs will still dominate in general - just like how CPUs still dominate despite ASICs.
They are fast, but they're still programmable accelerators, not a model burned into the gates.
If they designed this right, it means that once they have a model, so long as they keep the hyperparameters fixed they can change the weights much faster than it takes to spin up a completely new chip, essentially at a cost of doing a minor revision.
It takes about 18 months to go through the design, verification, and manufacturing process if you move at breakneck pace. Design could probably be sped up.
About 18 months ago the top model was GPT-4o. Not great by today's standards, but still good enough for many tasks (certainly a big chunk of chatbot queries). The current SOTA covers far more use cases, but importantly at a level that surpasses many thresholds of utility.
Maybe, but what is the shelf-life of that 18 month decision? Barely good enough today, when it launches, starts to get worse and worse every month going forward. You have to recuperate that investment on your depreciating asset.
And you are competing against anyone with the foresight to use a TPU instead, and the benefit from any new paper that finds how to distill, quantize or whatever better so their solution gets algorithmic boosts while you are locked in.
One could also imagine hybrid models, where part of the model is burned into ASICs and part of the model exists in VRAM/HBM2 so it can be updated.
I don't have enough low-level knowledge to evaluate the technical or economic feasibility of the above ideas, however.
Sometimes you need speed; sometimes you need quality. There are very different use cases for each. For my own workflows, I sometimes want something very simple done ASAP; other workflows need "subjective" reasoning and careful crafting of responses.
A model will often come up with worse results given more cycles of compute, only because it will tailspin from second guesses, rethinking and literal flip-flopping on concepts.
--- edit: to those following the thread below... if you look at the comments from the account replying, it's pretty obviously a pro-China account and all replies are antagonistic against anything other than a total submission to the Chinese state. My responses are intentionally antagonistic as every point I've brought up is completely ignored in favor of insults, so yeah, I've been insulting back.
If you let a model spin too much, you get worse results... that's a fact, even for the best models. Nothing you've stated since actually refutes that and acting like a paid bot doesn't mean anything.
I mean, if you want to felate Xi Jinping, have at it... that's all on you bro. I don't have a horse in this race.
You're just an idiot who assumes everyone that doesn't agree with you is the same. I'm surprised you're able to reply with Xi's penis in your mouth... even if it is kind of spacious in there for his limited size. How is the poo bear these days? Does he like it when you gargle his testicles in your mouth?
Yesterday there's a news on a breakthrough for probabilistic computer with 1 million p-bits [1].
Since LLM is stochastic in nature, this type of new computer can be much better than ASIC for processing LLM data.
[1] Biggest Probabilistic Computer Turns Noise into Answers:
This is not like a bayes model or something were it's distributions all the way down.
Assuming such work was happening.
Many AI uses are not that volatile. I had a 20 minute conversation today with some company's AI phone assistant. It was extremely good and would have been very helpful if any of the dozen people it tried to route me to would have picked up their phone. That AI won't need to be upgraded for a very long time. There is no reason for it to have a cloud brain except to force a recurring revenue for the company selling it.
Hardcore gamers are constantly throwing down insane money on the latest hardware. The rest of us can get by for a couple years with whatever we bought when the last one broke. Yeah, it's not the latest, but it gets the job done. I wonder if AI has not already reached the point where a gen 10 CPU--uh, I mean a v3 AI model--will get the job done for the next year. If I really need the up-to-the-second latest abilities for a minute, I can fallback to a cloud brain @ 1M tokens/$. Why pay a monthly lease on a 5-year plan for a 4-door Ford Ranger as your daily commuter? Buy a Clio and rent an F-250 twice a year when you need the hauling/towing capabilities.
Imagine if Anthropic could give effectively unlimited access to Sonnet, for $20. Wouldn’t that be an appealing option for many users? I know I’d make a lot of use of it for agentic tasks, office work, summarization, etc; when right now I’d save quota for more important tasks.
It is a bit like saying "why would you hire someone with a doctorate when you could get unlimited high school grads". How appealing that sounds depends on your needs.
The ability levels of the cheap models are encroaching on the abilities of the frontier models faster than frontier models are expanding their abilities. If we haven't already, we will very soon reach a "good enough" state where having the "best" model matters less and less and less.
By analogy, if you buy a new computer, do you get the absolute fastest CPU available? Maybe, depending on your workload. But if you're 90% of the population, you get the cheapest one that has enough power to meet your expected workload, which is mid-range, not top of the line.
Many of the responses seem to say "but there is a market for cheap models".
There is a world where we have cheap models and we don't have ASICs powering them. Things like TPUs and NPUs, which are programmable, are likely to fill that role. They are optimized for inference while also allowing different (and updated) models to run on them.
Given two companies competing on the cheap end. First company goes TPU, second goes ASIC: who wins? My bet is on TPU since they can update their model, even if their hardware is slightly more expensive and slightly slower, since the optionality of new models beats the performance gap. That may not hold forever but given the pace and volatility of the current LLM market, I believe it will hold for some time.
Models like Kimi 3, GLM 5.2, or even Fable 5 for that matter are reasonable to burn to.ASIC because they are over the threshold of "good enough to be generally useful", something that will continue to be true in the future.
Most people do not need the latest model, they need a sufficient model. If I had Fable 5 on an ASIC, I imagine I would use that and ignore paying API rates for Fable 6.
Which is why my original post mentioned the volatility in models. We aren't just doing research on frontier, there is a huge amount of research on quantization, distillation, etc. that is changing the landscape at the low-end almost as much as it is changing on the frontier.
And it is also why I mention revealed preference. What feels sufficient / "good enough" today is a moving target. This isn't just a question of what you want, it is a question of what is economically viable for the entity that will be designing, manufacturing and marketing this ASIC of which you speak.
This is me personally. The calculus is different for other people. But I suspect Kimi 3 is pretty darn close to that tipping point for an awful lot of people.
The managers of the firm will.
They dont care that its faster unless it translates into the financials. They want lower costs, higher revenues - explain how it fits brudda.
There’s absolutely a place for a lifestyle subscription to a sonnet model that you could just use everywhere all the time.
[1] https://finance.yahoo.com/technology/ai/articles/google-plan...