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.
Someday, I imagine model weights could even be encoded as analog resistors (memristors or similar) for even greater density
The trick is that every compute element in their system has it's own small pool of ROM, instead of putting all the ram behind a common pipe. ROM is just used because it's the densest kind of memory that can be fabricated on the same process as their logic.
Think it's called Askjimmy or similar.
(Not that I believe it, it writes too well for GPT-3.)
Hosted frontier models from two years ago would be much faster today, too.
Less so for consumers though, because it'd mean the phone is out of date in 3 months when a better model comes along.
Now you sell the same phone with higher price tag.
At some point the music will stop on training bigger models, and when that happens it will make sense to have ROM weights (or 100% analog circuits given how noise-resistant LLMs are), but we'll know when that is because the investment bubble funding the training of new models will have burst.
The rate of change to the models has to be slower than the hardware roll-out to be worth a hardware solution. If "good enough" happens before then, that just means the user gets a software solution.
The rate of change by itself doesn’t tell you the whole story because of costs and diminishing returns. So what if your model is twice as good if it’s 10x the cost and it saves you 1ms? Everything else about phones reached “good enough for a phone” levels in years, and then got minimal generational improvements.
The reason we don't do this in general (any more) is that for long chains between input and output it has been much too difficult to avoid accumulation of errors. LLMs happen to be extremely resilient to errors like this, which is also why we can use e.g. 4-bit weights.
That's the only thing the normie consumer cares for really.
Ofc if the model has some critical bugs that’s another matter.
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.
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
Can you say this to it: "Hey Siri [wait for it to come up] - please send me an email with the temperature right now so I have it for my records." and see if it can complete the task without any backtalk or misunderstanding, and if you get exactly what you asked for. (It's a really clear request.) Should be 1 statement, no clarification, conversation, random search results, ("Here's what I found!"), etc.
A normal frontier model can do that - or Siri can do it if it is properly connected to Claude, ChatGPT, Gemini, Grok, or any other frontier AI - but previously it was never properly connected.
If it can do this task, I might have to look into this again. It counts as a success if it sends yourself any email with the current temperature and you actually get it (it can include whatever other text in the email), and a failure if it talks back, says "here's what I found", says it can't, asks you any question, sends you an email that doesn't actually contain the current temperature, just reads you the temperature and then asks if you want it to send an email, etc. Should be 1 shot.
let me know if it works!
Subject: Current Temperature Body: The current temperature is 27°C in <my city>.
Once they started seeing useful (if niche) functionality as a cost center, there wasn't really a world in which these could usefully exist. Their big bet now seems to be that LLMs will lead them to profitability - but whether that's from increased data harvesting, cheaper integrations, or because it'll be useful enough to charge subscription fees, I couldn't tell you.
What Siri is missing is more logical solutions and answers for recipes, etc (still suck even with chatgpt integration).
"Hey Siri, what's the weather in <nearby town with a generic name> tomorrow" and it gave me a town with the same name ~800 miles from me.
Add a bunch of chips together, and you get to a server that can run a 800B model, very fast and probably significantly cheaper than others.
[1]https://www.eetimes.com/taalas-specializes-to-extremes-for-e...
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.
This tech can definitely scale up from the current 8B prototype, but - at least as far as my limited understanding of the tech involved goes - you cannot just ASIC a trillion weights model due to physical size constraints.
___
Specification HC1
Model Llama 3.1 8B (hardwired)
Process TSMC 6nm
Die size 815mm²
___
So the current prototype already pushes the limits of what we can fit on a single die, and that is already likely going to limit your yield.
- Deepseek V4 Flash is impressively capable. Sonnet still beats it out by a thin margin, but the real kicker is that a typical session with Sonnet at current API costs is ~$2. The same session with Deepseek is 2 cents (ha). Its even allowed me to consider offering free-with-limits API usage on my own app. - Taalas (or competitors) have a lot going for them. If anything I feel like they need to join hands with these smaller model makers and converge in 2028
If you could manage a per-die expert somehow and keep the expert routing gate relatively fast (through an interposer interconnect or doing wafer-scale Cerebras type shit) you don't need to keep the whole thing on the same die. Small dies with one expert per die on an interposer, and a very tiny router might be sufficient.
Of course, 1T SRAM isn't really SRAM, but my understanding is it doesn't require external refresh like eDRAM, is a bit easier to fab on-die than eDRAM, and is half the mm2 per Megabit compared to real SRAM (15% more die size than eDRAM)...
edit: Ok, I will self-apologize. Its apparently a 3B model. Mighty impressive for what it does.
Where I work we invited a bunch of people circa May 2023: a few top tier academics, a few government and NGO officials in charge of our industry, and a few startup founders. We are a big company, so people were kind enough to come and give speeches - and discuss.
This was a roundtable on what's going to happen. You could play some of the speeches verbatim today and they would not feel out of place. And that is telling. We had a Stanford professor saying that gpt-3.5 can do everything he can - just better, and he feels his profession is on borrowed time. We had a government guy saying that entire swaths of jobs will be displaced before the year ends. And so on.
The interesting thing is, a lot of people believed it then - and a lot of people believe it now. I wonder if we will have the same deja vu in, say, 2029. No, for sure not. It's going to be done and dusted for human thinking by end of this calendar year.
If you have the names, so we can record them in the History of Laughing Stocks...
Also remember maybe all those who shouted "that is really, precisely unintelligent".
Incidentally: also Sam Altman, in an interview with Lex Fridman, said "that is not proper intelligence but currently we do not know how to get there" - if very Altman refused to call it AGI...
But if the basic premise of "good enough LLM at insane throughput" holds, I think it could qualitatively change local uses of LLMs. At a certain speed point, you're able to move from request -> response to a cascade of tool calling and "subagents", which could allow a small model to be much more useful, if provided with a lot of local data and tool calls.
That said, this is assuming you could stuff a "good enough" model into a phone with Taalas-like technology. The Taalas tech demo was an 8B parameter model and required hundreds of watts (IIRC) to run. The efficiency was good given the speed (as I understand), but it's not clear at all that the approach scales small enough to be a sensible coprocessor on an iPhone or whatever.
That also needs server class hardware though. A phone won’t happily service the insane amount of IO, compute, and network that this cascade would require.
Raw intelligence becomes slightly less important when you can iterate and improve automatically. You can still claim it was "one shot" even when 30 different implementations were made then combined.
Ok, real life example: I now spend most of my time, as a developer, waiting for the agent to do its thing (after careful prompting, I'm also thinking about work stuff, don't worry I'm not useless). What if it gave back the same excellent results, but instantaneously? Why, then, I certainly would become the bottleneck. So, quite possibly, my last work task would be to plug this agent directly into the ticket system where the domain experts input their feature requests. Maybe we still need 1 developer out of 100, to coordinate releases and all that (ok, say 1 out of 10).
But that's not taking things far enough: why do we need these domain experts at all? Our pitch is clear, and all software-enabled, though it took years to develop. We can just have the clients express their concerns to the AI, directly or indirectly. Have multiple lighting-fast agents with different roles (refactoring agent, new features agent, debugger agent, domain expert agent, etc.). So we fire everyone, maybe keep 1 product owner / devops to keep the trolls out. The cost is still probably 100 times less than it used to be (beyond the initial cost of acquisition of the magic machine or whatever).
But one of these clients, surely, will realize that these 10 years of manual and slowly-automated development can now be emulated in very, very little time. Why not just, say, take screenshots of the entire app and feed them into the magic machine? Why, this way, they could have the service for a tenth of the yearly cost, forever!
And then the economy implodes.
I'm not saying it's THE most likely version of things, I'm saying that at a certain level, quantity (or rather, speed) is a quality all its own. And this new quality might change the world. Let's hope it's for the better!
That said, it obviously depends on the project.
A frustrating vision of the future would be when we've been asking for faster loading lighter web pages for years and then companies start caring about it and improving it not for us humans but for LLMs.
you need to launch 10-15 more terminals, who is waiting these days? :)
AI previously provided speed but not quality. As soon as quality reached an acceptable threshold, the speed became the reigning factor.
In my opinion the quality is still much lower, but speed means the cost is significantly lower also.
I mean, if an agent can do half-decent work in less time than it takes the user to prompt them (and "user" in this context is a fast touch-typist like most programmers are), it's obvious it's not the agent that's the bottleneck anymore.
This has always been the case for human project management, and LLMs just aren't at that level yet.
It's more like everyone is speed running to how fast they can convince others that "half decent" is good enough. And for sure, newer models of LLM seem to be getting better at that.
But that's what Agile is all about, isn't it? We've been speedrunning delivering increasingly smelly shit at increased velocity ever since SaaS became a thing, because ubiquitous Internet access is what allowed our industry to adopt the "lob feces over the fence for users to deal with" release model.
AI does speed that up, true (though since the market - and management - didn't catch up with it yet, we have a brief moment where we can use AI to increase quality while keeping usual delivery rate.)
If inference speed goes up, I can launch the same query 5 times, evaluate the best result and proceed from there. Of course, evaluation is also instant, so in seconds I can get a near perfect solution. Or maybe 10 and I can pick what I like the best.
It would certainly be an accelerator for people who know exactly what they want. And it would remove multi tasking, which I‘d appreciate.
This is the "dumber but honest person that works harder" phenomenon, vs "lazy genius".
Sure, in the future full rewrites and stuff like that will be just another "throw money at it" problem, but fundamentally software can get arbitrary complex and we barely know how to write large, maintainable code bases.
Nonetheless, I think testing (and maybe proofs) will have its long-awaited time to shine, as being the "reward function".
Right now, I put models in low thinking mode during my refactors and hate waiting. I would much rather have a faster model that that maybe was slightly stupider, and I would wait far less long between prompts where it needs my valuable input.
Models that are dumb, but humble and fast, can be fine.
Obviously a CTO is not going to walk away from the technology just because it's not good enough. That much more incentive for someone to create a powerful enough harness that can direct that power safely and productively. Like a nuclear core, we'll need to come up with the graphite rods and water tank. And if tokens are essentially free, why not, for every million tokens, spend 10x tokens on code review, testing, etc?
Whatever you can cheaply do with AI is not a moat, if there is profit in there there will be quick imitation and competition will eat away those profits.
Models can be replaced easily, harnesses & AI tools too. And if cloud inference gets too expensive there are local models keeping the cloud prices hard capped.
Probably AI won't make anyone very rich.
This model had zero information right, while being fast in responding.
Unacceptable.
> Bruce Lee was born in San Francisco, California, USA on November 27, 1940.
> Bruce Lee's father was a Chinese opera singer
That being said, this is not a good test. It is a language model (a very small one), not an encyclopedia.
ChatJimmy interface is just a tech demo. Without tool calling functionality we can't expect it to be factually correct.
This will generally make them suck, though, a little bit of randomness is necessary for proper function.
I just pasted your comment and its whole inheritance chain to it, started my comment, and asked to generate a total of 9 completions, 3 from each of {current & next word, current paragraph, current paragraph + rewrite the entire paragraph}.
Half of the answers were perfectly good (ironically, not the "next word" ones!), but the important bit, they came back near-instantly ("Generated in 0.024s - 14,163 tok/s", the page says). Slightly more powerful model while keeping this under a second, and this could easily become a qualitatively different form of autocomplete/text suggestion. Running in the background every couple keystrokes, or every time user stops typing for more than 500ms.
>I just pasted your comment and its whole inheritance chain to it,
Good idea. Only problem is it doesn't work. I just did the same thing with exactly this prompt:
>did the user IOT_Apprentice participate in the thread below and if, number and quote all of their comments. Only just number and quote the comments or write "Did not participate", do not add any commentary. Quote any comments by this user verbatim, exactly as input. Thread:
followed by pasting the thread[1]
And received the answer "IOT_Apprentice did not participate in the thread."[2] in 0.001s, even though they have literally the last comment in my quote and it's clearly legible.
It's particularly insidious because the understanding and thinking that is required to follow my requested answer format exactly is substantial - so based on the fact that it gets the format right and clearly understood the assignment, I would be inclined to believe that it would also be correct!
So to use your example, it's not just autocomplete, it's autocomplete that confidently returns "No matching results" in 0.001 seconds, even though there is a search term matching what you put in, right in the prompt itself that was sent to it. That is much worse than useless.
[1] prompt: https://ibb.co/CKVmRvtd
[2] result: https://ibb.co/BKdRKmyD
Fully interactive realtime NPCs in videogames at scale.
Recommender systems that simulate individual consumers.
Crazy shit
In a way... when it's finance, they should be maybe called Gray'ish Swans?
Better yet use it to dimulate counterfactual phenomena like market manipulations ypu intend to enact...
Autocorrect that works. Reply suggestions that almost work, just need to be tad more accurate (probably more of a data access issue than model) and a tad faster to look completely seamless. Screenshots with automated text detection and OCR and automatic interpretation (different suggested actions for when something on the picture looks like a web link, phone number, postal address, e-mail, or QR code, or an event poster). That's just a fraction of things I saw showing up on my Samsung phone over the last 6 months.
For over a year now, you could get a much better autocorrect and spell/grammar check, and a translator all in one, if you just pasted your text to a frontier model and asked it to check for errors or translate into target language. Now imagine being able to go through a round of such checks in a 1/100 of a second. You could have this running every keystroke, and suddenly the inline autocorrect/checks would not suck anymore.
Auto-linkifying that can correct for typos and doesn't need careful regex tuning because it understands from context what is meant to be a link or not. That's just one of many obvious things possible once you get local models running fast enough. Tip of an iceberg, and the first step to imagining all the other potential uses is to let go of the two mistaken beliefs people hold on to:
1. That LLMs are about written language. They're not; ever since "multimodal models" became a thing, tokenization extended to visual and audio space, and now textual and visual and aural inputs are all just regular, first-class tokens.
2. That chatting with the models is the only optimal way for end users to interact with AI. That's just artificially limiting yourself to the space of chat-based UI.
8B model (FP4) = 4 GB DRAM = 32 Gb DRAM = 80 mm2
8B model (Taalas) = 4 GB ROM = ~800 mm2
I could see it being feasible to get a Qwen-3.6-27b type of model done on something like this. Qwen-3.6-27b at 18tok/s would be a game changer.
It is more that there are multiple reasons why this idea (burning an LLM into silicone and deploying it into a device in people’s pockets) requires huge piles of cash and the kind of engineering chops only a few company posesses.
Of course i would like it if a small upstart would do this, but it doesn’t seem likely as a posibility. They won’t have the funds to fab the IC. They won’t have the funds to train and validate the model before burning it into silicone. They can’t absorb the risk of the first tape out going wrong. They can’t absorb the risk of the model being faulty in some subtle way. They don’t have a device to integrate the IC into. They won’t have the funds to develop one. If they somehow would make a device they don’t have the marketing and sales channels built out to get the device into people’s hands in sufficient numbers to justify the development cost.
Basically this idea feels ruinously expensive. Apple has deep pockets, they already have working well-regarded phones, and an ethos of privacy preserving innovation. This is why this idea feels well suited for them and not many others.
Do i want the winners to keep winning? No. But not many others can pay for a moonshot crossed with a manhattan project. They just can’t.
Right now it's kinda the world we live in, with Apple or Google doing the total vertical integration from chip design to retail shops, but it doesn't have to be that way.
It could be done the traditional way with for instance a joint venture receiving funds and expertise from several players in each of the field and collaborating with external companies to get to the final package.
I don't think it needs to be on phone per-se. It can keep chugging in cloud - plenty of people use cheaper older models.
And I suspect the growth will slow eventually making taalas interations slower.
I expect this to be around the time when we're finally ready to travel to Mars.
A ~30mm side for the HC1 tech for an 8b model (still unclear the planned HC2)?
... you would still have a mediocre phone with half-assed barely working features driven by locked down proprietary software