In my regular research (behind a paywall), I have been saying for a while that I think the future of AI is not large language models (LLM), but small language models (SLM) run on local desktop computers or even mobile phones.
I’m really interested to see what comes of the LLM ASIC idea - it worked wonders for hardware video codecs. I could see the smartphone industry picking up again on annual releases worthy of upgrades if they included dedicated AI chips not beholden to a cloud subscription. I think we’re reaching the point that the frontier models are becoming “good enough” to use for a while before uploading to the next.
That’s kind of what I’m expecting going forward too. Local models will get good enough in a year or two for most tasks, and then you just have a specialized chip like the GPU to run them.
A specialised chip wouldn’t be nessesary though. GPU’s can run smaller models just fine the main issue is enough memory to store larger ones without resorting to CPU memory. Power consumption probably is the main gain to be had with NPU’s. You’ll still be limited by slow cpu memory once you run out of faster memory.
Oh didn’t see that. This is really cool! I suppose it does work similarly to hardware codecs, with the same very big trade-off being you get locked into a specific model. But considering this is an emerging technology maybe they could be made small enough to have multiple models on a single chip! (similar to codecs) Or just have one really big model that would be more future proof, and the price-performance would be so much greater than running the model on a GPU.
LLM ASICs are different to NPUs. The idea is to encode a specific model’s weights (all billions of them) as a gigantic resistor network on the chip itself. Analog, where ohms law becomes the processor, and no RAM is necessary - the chip IS the model. It would pave the way for cost effective, fast, low-power local inference. Given the analog circuitry, there would be issues like interference, temperature sensitivity and so on, but we’re seeing models get good results even when heavily quantised, so the idea has legs. The big disadvantage is you’re stuck with that one model forever, so we’re likely to see these first appear in non-consumer applications like automotive, defence etc. A pretty exciting concept that solves some of the environmental issues around AI.
Alibaba just announced a chip specifically for running local models. We’ll see what it ends up going for. https://wccftech.com/alibabas-tsmc-built-5nm-risc-v-chip-xuantie-c950-now-runs-qwen-3-8-27b-model-natively-unlocking-massive-vertical-integration-tailwinds/
I’m really interested to see what comes of the LLM ASIC idea - it worked wonders for hardware video codecs. I could see the smartphone industry picking up again on annual releases worthy of upgrades if they included dedicated AI chips not beholden to a cloud subscription. I think we’re reaching the point that the frontier models are becoming “good enough” to use for a while before uploading to the next.
That’s kind of what I’m expecting going forward too. Local models will get good enough in a year or two for most tasks, and then you just have a specialized chip like the GPU to run them.
A specialised chip wouldn’t be nessesary though. GPU’s can run smaller models just fine the main issue is enough memory to store larger ones without resorting to CPU memory. Power consumption probably is the main gain to be had with NPU’s. You’ll still be limited by slow cpu memory once you run out of faster memory.
Read my post above to a different commenter, there’s a new type of ASIC being talked about that’s very different to an NPU or GPU, no RAM necessary.
Oh didn’t see that. This is really cool! I suppose it does work similarly to hardware codecs, with the same very big trade-off being you get locked into a specific model. But considering this is an emerging technology maybe they could be made small enough to have multiple models on a single chip! (similar to codecs) Or just have one really big model that would be more future proof, and the price-performance would be so much greater than running the model on a GPU.
Also I can wait for Nvidia’s stock price to completely die as soon as this becomes mainstream.
NPU’s have been in phone SoCs for years now.
LLM ASICs are different to NPUs. The idea is to encode a specific model’s weights (all billions of them) as a gigantic resistor network on the chip itself. Analog, where ohms law becomes the processor, and no RAM is necessary - the chip IS the model. It would pave the way for cost effective, fast, low-power local inference. Given the analog circuitry, there would be issues like interference, temperature sensitivity and so on, but we’re seeing models get good results even when heavily quantised, so the idea has legs. The big disadvantage is you’re stuck with that one model forever, so we’re likely to see these first appear in non-consumer applications like automotive, defence etc. A pretty exciting concept that solves some of the environmental issues around AI.
More info for the curious: https://taalas.com/the-path-to-ubiquitous-ai/