Comment by moshun
8 hours ago
Considering the rate of model development and rail hopping, seems like baking models into silicon is speed-running obsolescence.
8 hours ago
Considering the rate of model development and rail hopping, seems like baking models into silicon is speed-running obsolescence.
If you’re only running models for frontier capabilities, yeah. For tasks where current models are smart enough, running them 100x faster is the most impactful improvement you can make. Consider all the things you could use a model for, but don’t, because the latency is just a bit too high.
I'd gladly pay for a Claude Opus 4.6 Thinking High in silicon and use it for 1-2 years. It's good enough for many coding tasks.
But Claude Opus 4.6 is not really practical. Taalas' process seems targeted for edge models. Their proof of concept model, for example, is a heavily quantized version of Llama 3.1 8B and even then they acknowledge their custom 3-bit/6-bit representation causes model quality degradation.
Taalas is going to have a tough time putting a trillion-parameter model on one conventional die. Their HC1 die is already near the maximum size that conventional lithography can expose. They claim they could partition the model across many chips, but I'm not sure if they have tested this process or what it means for compute. The basic storage arithmetic is unforgiving: for a one trillion parameters model at four bits it will take 50–100 chips. To service a sizable customer base will take thousands of 100-chip fabs.
That all said, I'm bullish on this technology, and look forward to seeing it evolve.
A really fast qwen-3.6-27B type of model could be useful. With a specialized harness and this speed I 'd expect it to find many applications. Implementing a coding plan is the minimum I can think of.
Yeah. But this kinda feels like a bandaid.
Eventually someone will have to solve compute in memory at scale.
With thousands of token per second output it would be an enormous waste of resources. Such chips are clearly made to process thousands of conversations simultaneously. Not necessarily in parallel. All LLM workflows are turn based right now, there are often seconds between turns until tool calls finish or users type the next message.
If the LLM response only takes a few milliseconds, the chip can process hundreds of other requests until the first conversation becomes active again.
Not so long ago, I was good enough for many coding tasks. But I found that things can change in a hurry.
Yes, a cheap and fast Opus4.6 can drive a lot of value in current context. But if we continue to craft bigger-and-bigger balls of mud, Opus 4.6 may end up hitting its conceptual ceiling and unable to contribute.
Winding the clock back on your statement gives:
> I'd gladly pay for a Claude Sonnet 3.5 in silicon and use it for 1-2 years.
Man, I dunno.
Assuming moore's law like progress, which I'm 100% sure isn't going to happen - I think we're at the top of the S curve already. But assuming dramatically increased intelligence every year this is still the exact same position as anyone who bought a computer in the last 5 decades. Yet, people did very much buy computers.
It costs something like $300,000 for the hardware to run a model of that size. You'd pay that for a single model for 1-2 years? Not even the AI companies can justify that kind of spend which is why they keep extending the expected lifespan on their hardware in the accounting.
I'm expecting the Taalas MSIC version to cost a fraction of that. Then probably have some kind of cheap subscription to Anthropic for updates (yes, Taalas chips can receive a certain kind of updates: they have a small SRAM).
> It costs something like $300,000 for the hardware to run a model of that size
You did not compute that as the cost for a speculative card from Taalas, right?
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Depends on how much it costs the consumer. If I could buy a "cartridge" of Kimi K3 for 300 bucks I 100% would buy that shit asap. Even if it's "no good" after lets say 4 months still would be worth it IMO.
That's definitely super-enthousiast territory. Paying 80 bucks a month for AI is more than 99.99% of people would be willing to do
This will be considered very cheap within the year IMO. The value you get from AI is exponentially increasing and like all tech just takes some time to ramp up. Cell phones, internet and many other amenities when they came out many people were not willing to pay for but that all changed and considering how important AI tech is this will also be the case especially considering if its 100% private such as for that cartridge.
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That's because the super-enthusiast will upgrade in 4 months when a better model is released. The casual user would keep it for years. A year of claude at the lowest plan is almost $300
It depends on how quickly you can bake new architectures.
Text diffusion might be a disruptor here, but let me just say the most cutting edhe form of image diffusion (JiT and DiT) right now is just a big fat stack of alternating attention and MLP matmulls. Not theoretically hard to bake
"seems like baking models into silicon is speed-running obsolescence"
Now maybe. When models are flying passenger aircraft, other prerogatives will assert themselves. When a 50TB ROM means you can impulse purchase a ChatGPT 6.3 xhigh that runs on batteries, yet more use cases will be apparent.
Well, 50TB ROM Taalas HC1 style would be apparently a 400000b transistor system through a chip sized 2.5 meters on the side... :)
Yes but have we considered employing, like, a really big block of ice? Like old-timey surgeries? What if we put a big block of ice on the 2.5 cubic meter CPU what happens then?
Phones were getting too thin anyways.
Or autonomous weapon systems, missiles, and drones.
Why would they need multi TB frontier models?
I could see this making sense when model development start to settle down ... it's going to settle down, right? ...
Not sure. You can fix the transistors but leave the connections between them open for flexibility, so you only need to change the manufacturing process for the upper masks for every new model.
Surely that added flexibility negatively impacts the density/parameter count of the model you could etch?
Or do a hybrid
Compute the cost of producing n of them devices, imagine a fair price based on that, and see if that local, blazing fast card* can be an asset that could be replaced periodically.
*(It's local: private files managing firm oriented. It's blazing fast: it can be placed into recursive, intensive local workflows.)
Which is exactly what companies and shareholders want to increase sales.
Look at it the other way: compared to the cost of training a model, the cost of making a custom ASIC is trivial.
obsolescence is the whole point. apple gets to sell a new phone very 6-12 months because of it.
i have written about this:
"For device makers
Packaging models with laptops and smartphones will let application access near free, low latency inference and potentially offer users a better experience with the option of preserving data on-device. This is viable under the condition that tasks that do require larger expert models that run in the cloud can be routed to external models. A side-effect of local models and what will let Apple cut upgrade cycles from ~4 years (?) down to 12-18 months is specialized hardware to run them. For almost a decade, smartphones have been trying to compete on better cameras. This coming decade will see them selling better GPUs, NPUs, ASICs and whatever other things they'll be calling the inference chips, to drive re-purchase. Every six months will see a better model on new hardware, which will enable better performance in certain applications."
https://try.works/role-model-the-case-for-a-model-routing-pr...
No, the point is inference speed and power.
you don't understand what I wrote.