alt Hacker NewsThe 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.
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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.
> Cerebrus/Talas
They are fast, but they're still programmable accelerators, not a model burned into the gates.
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.