Comment by akiselev
1 day ago
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.
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.
> 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.
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.
> 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."
or, hear me out, advertisers can do real-time advertising based on hyper-now context
You will be very happy once OpenAI serves GPT-5.6-Sol on Cerebus at 750 token/s.
What you think doesn't matter unless its strictly for personal use.
Your company will decide what makes economical sense.
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.
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.
This is where treating these smaller models more like parts in a purpose built appliance ultimately benefits us.
> Cerebrus/Talas
They are fast, but they're still programmable accelerators, not a model burned into the gates.
Cerebras is a programmable accelerator, but Taalas does burn the model into the gates.
Ah, right. I misremembered. Does Taalas have a meaningful advantage here then?
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