[stillpointlab]

I'm not convinced, mostly because things like crypto, which I believe went into ASICs, were based on very slowly moving and mostly understood algorithms. LLMs and model architectures seems significantly more volatile. I wouldn't want to be working out the finer details of my chip rollout only to find a new paper/approach that give multiples of performance.

So I guess it depends on how much the latest-greatest model motivates people, and my read on the current churn is that developers are extremely unloyal to brand at this point and will jump to whoever has the best model. And as long as the best model is running on programmable GPUs, that will be the dominant form.

[akiselev]

The tokens per second performance numbers coming from Cerebrus/Talas are several orders of magnitude higher than models running on GPUs, which is such a huge step change that it will enable many more uses of LLMs that are impractical otherwise. I.e. think about gamers and burning in an LLM chip on a game console like a future Play Station - it doesn't matter if its a frontier LLM if it allows them to talk to in game characters in real time and have the LLM drive the storyline. The devs may be limited to training/RLHFing legacy models, but the performance enables many more use cases.

[stillpointlab]

I admit I would like a faster model - but even though I have faster models available I still go to Fable or GPT-5.6 90% of the time. So there is a gap between a potential preference and a revealed preference.

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.

[breuleux]

> even though I have faster models available I still go to Fable or GPT-5.6 90% of the time

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.

[invalidusernam3]

Exactly, I think a lot of people aren't thinking about it this way, they're imagining a faster version of ChatGPT. In reality if it was a frontier model running a these speeds it would change so much about how we interact with computers. It would be custom hyper specific software on demand.

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.

[prirun]

> hook it up to your browser and have it yank out all advertisements live

AI: "I'm sorry, a security guardrail prevents me from performing this operation."

[itemize123]

or, hear me out, advertisers can do real-time advertising based on hyper-now context

[breuleux]

Yeah... savage capitalism will ruin this too :'(

[super256]

You will be very happy once OpenAI serves GPT-5.6-Sol on Cerebus at 750 token/s.

[sdfefcxv]

What you think doesn't matter unless its strictly for personal use.

Your company will decide what makes economical sense.

[dehugger]

Some of us happen to be in positions to make those decisions at our respective companies. What we think does, in fact, matter.

[aurareturn]

I don't think ASICs is the long term answer here at the local LLM level. I think GPUs will still be.

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.

[quotemstr]

> Cerebrus/Talas

They are fast, but they're still programmable accelerators, not a model burned into the gates.

[neynt]

Cerebras is a programmable accelerator, but Taalas does burn the model into the gates.

[quotemstr]

Ah, right. I misremembered. Does Taalas have a meaningful advantage here then?

[lsaferite]

Taalas claims significant cost, speed, and power differential with running on Nvidia (and similar) hardware. Their whole thesis is that with making the hardware cost less, run faster, and run with less power mixed with their claimed fast production cycle for new weights, companies would plan on purchasing and then relegating the other chips to different workloads at the next update cycle. And the chips have facilities for fine-tunes that can by dynamically loaded. The info I've seen has been very light on details, but if they can even get halfway where they are planning, it'll be a massive shift.

[wyre]

6x. Taalas has Llama3.1 8B running at 18000 tok/s. Cerebras advertised that model at 3000 tok/s.

[quotemstr]

Interesting. Is the speedup from specializing for the shape of Llama 3.1 or are they (contra my mental model) actually winning on burning in the weights?

[HNisCIS]

8B is still three orders smaller than current frontier though

[brian-armstrong]

The models would have to get significantly better for this to help, though. Unedited LLM dialog is quite bad. Though, I wouldn't put it past AAAs to try anyway.

[F7F7F7]

This is where treating these smaller models more like parts in a purpose built appliance ultimately benefits us.

[LarsDu88]

Honestly, whether you think burning current SOTA to hardware is an overinvestment risk depends on what your definition of intelligence is. If you think intelligence is something that can grow like height such that 18 months from now we will basically be bowing down to machine god giants that are running on B200s, then investing in ASICs is the wrong move. However, if you you subscribe to the (very reasonable view) that intelligence is more like a round ball that we are trying to make as spherical as possible (ala Francois Chollet's writings), then at some point the ball will be smooth enough for most people and many tasks.

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.

[stillpointlab]

I think part of this thinking is in reverse. What I mean by that is people are saying something like "Imagine if 18 months ago a company started to create an ASIC based on GPT-4o (or whatever was good at the time). By today they'd have a model that is not great by today's standard but good enough".

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.

[JohnBooty]

At a 50-100x speedup even a GPT-4o class model could perhaps compete with much newer models simply by thinking deeper, doing harness-controlled Ralph loops, etc. Sure, then it might be "only" ~2-5x faster, but, you wouldn't need to throw all the ASICs into the trash bin.

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.

[boplicity]

For many, many tasks, you only need good enough results that accomplish a clearly-defined set of criteria. Smaller models that run 50x faster, in this case, could be far superior to a much slower model that meets the same criteria.

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.

[tracker1]

Giving a literal monkey the ability to press more keys faster doesn't get a good joke from it.

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.

[JohnBooty]

    Giving a literal monkey the ability to press more 
    keys faster doesn't get a good joke from it.

To some extent, it's because making a joke isn't an iterative exercise with a verifiably correct outcome.

However, a lot of jobs are. Notably, much of software engineering.

[sdfefcxv]

Said the OAI/Anthropic stockholder

[tracker1]

LOL.. neither... but good luck on your standup career with jokes by literal monkeys.

[freeopinion]

I thought about the volatility, too. But here are some additional thoughts:

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.

[drob518]

The churn is an issue. For a while there, it felt like we were getting a new number format every month or two (e.g., fp4, ternary, etc.). That level of innovation works against moving things into hardware, or at least you need to be willing to spin hardware constantly.

[tracker1]

Even then... Do we now stop the with to asic DeepSeek and do k3 instead?

Assuming such work was happening.

[drob518]

My personal feeling is to not move to ASICs just yet. Things are still pretty frothy right now, so I would probably wait 6-12 months. At some point the froth always calms down. At that point, commit to ASICs. That said, I’m also not totally sure exactly how much the ASIC hard codes vs having some wiggle room. The Taalas site is a bit vague as to exactly how they encode the model.

[tracker1]

I would probably wait until a year goes by without any significant advancement, at the very least before making such an effort.

[alhirzel]

I think it depends on how good is "good enough". Personally, I think as long as tasks are not coding, there would be a ton of "good enough" value in having your own personal Google on a hardware device. I think the company that designs and licenses the architecture that packages the silicon for the largest market share will be the winner in commercializing this product. Wouldn't be surprised if this is part of Apple's roadmap to retaining existing customer loyalty.

[teleforce]

>I'm not convinced, mostly because things like crypto, which I believe went into ASICs, were based on very slowly moving and mostly understood algorithms.

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:

https://news.ycombinator.com/item?id=48971938

[nightski]

Except LLMs are not stochastic in nature. Correct me if I am wrong, but it's just the final layer which outputs a distribution across the output tokens. In reality, the rest of the model is deterministic.

This is not like a bayes model or something were it's distributions all the way down.

[gigatexal]

Apparently Google and team will build ASICs to run Gemini even though they have TPUs and it’ll be somewhat dynamic since the weights can be made modular [1]

[1] https://finance.yahoo.com/technology/ai/articles/google-plan...

[alex43578]

I think the gamble comes down to how many tokens need to be served on your best model, versus how many can be served in the cheapest/fastest way.

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.

[stillpointlab]

I mean, if I imagine Anthropic giving away unlimited Sonnet 4.5 away at $20, I would still be paying the $200 for fable.

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.

[margalabargala]

Right, and while there are needs that require a doctorate, having unlimited high school grads would be immensely useful for many many tasks.

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.

[stillpointlab]

I think there is growing confusion in this discussion. I am saying "I see the fixed nature of ASICs creating a barrier to their adoption despite how cheap they are".

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.

[margalabargala]

The reason you are getting "but there is a market for cheap models" as a response, is people saying in a roundabout way "but soon models may not need to be updated".

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.

[stillpointlab]

> but soon models may not need to be updated

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.

[tracker1]

How much compute does the cheap model require to operate? Power + hardware amortized over a couple years.

[alex43578]

It’s like how most people on here would want a loaded MacBook Pro or RTX 5090, but chromebooks and iGPUs do volume.

There’s absolutely a place for a lifestyle subscription to a sonnet model that you could just use everywhere all the time.

[stillpointlab]

Yes, but the context of the discussion is "who wins the war and how". And you are moving the goal posts very far from the original claim, which is that the speed with which companies can get their models onto ASICs will be the determining factor.

[vel0city]

What percentage of white collar work these days requires doctorate level thinking all the time?

[drob518]

Sure, but you’re undoubtedly the tip of the spear. Lots of people don’t need that.

[gavin_gee]

seems to me that we are at the asymtote for most usage. sure run the prompts that need the frontier on generic silicon but burning the fable 5 model into silicon could be perfectly viable