Taalas HC1: 17,000 tokens/sec on Llama 3.1 8B vs Nvidia H200’s 233 tokens/sec. 73x faster at one-tenth the power. Each chip runs ONE model, hardwired into the transistors.
Taalas HC1: 17,000 tokens/sec on Llama 3.1 8B vs Nvidia H200’s 233 tokens/sec. 73x faster at one-tenth the power. Each chip runs ONE model, hardwired into the transistors.
Dedicated, single purpose, chip designs are always going to be faster and more efficient to run than general purpose ones. The question will be what the environmental, and financial costs will be of updating to a new model. With a general purpose design it’s just a case of liading sone new code. With a model that’s baked into the silicon you have to design and manufacture new chips, then install them.
I can see this being useful in certain niche usecases where requirements are not going to change, but it sounds rather limiting in the general case.
A lot of the models we have are about as good as they are going to get. I mean, ChatGPT 5 isn’t appreciably better than ChatGPT 4. Hook one of those models or even one not as strong to a purpose-built RAG pipeline and a controller to run as mesh of interconnected prompts and agents, and you’ll blow away general purpose chatbots in niche areas in terms of cost, efficiency, and performance.
The question then becomes, to what purpose can you put this super fast, dedicated machine that performs certain small-scopes, simple tasks really well, but also fucks up often enough that you can’t depend on it. To what tasks could you set a bot that does stuff with minimal competence let’s say 90% of the time, and the other 10%, doesn’t create even bigger problems?
That domain exists, but it’s thin and narrow.
Sounds like a typical human to me.
A chip like this would be perfect for an autonomous robot. Drone, humanoid, whatever - something that still needs to be able to handle itself when it’s cut off from outside control. Always nice to have an internet connection to draw on a bigger, more capable “brain” somewhere else, but if that connection is lost you want it to be able to carry on with whatever it’s doing and not just flop over limply.
Sure. It excels in cases where 60-90% success rate is better than nothing. If you have a smart mine that doesn’t detonate on civilians, 50% success is better than 0. It reduces civilian casualties by 50%, which is still awful, but if you’re going to plant mines it’s better than entirely indiscriminate. Use cases definitely exist. A false positive means it doesn’t detonate on one soldier but might on the next — still an effective deterrent. A false negative means it blows up a kid, which a dumb mine would also do anyway.
It’s just generally not in situations most people are generally thinking about. You have to imagine cases where there is some upside and no downside. It doesn’t work in a context of say, auto-breaking a car if a pedestrian is detected because a false positive is going to cause accidents and probably kill people even if in other circumstances it does save lives.
A lot of ai hallucinations can be resolved by simply running the results through additional prompts automatically, then checking the various results against each other or against reference material.
Many agentic systems already do that with a limited number of follow up/check steps, but they’re often restricted by acceptable response times or just sheer costs.
I managed to get copilot in excel to run a 43 prompt chain in just a little under 10 minutes the other day. The result was exactly what I needed.
If you have 73 times the output, you can potentially afford to do that kind of processing in an acceptable time frame and cost level.
Why doesn’t it work in those contexts? It’s better than nothing in those contexts too. I’d rather have a car with onboard intelligence to take over than an uncontrolled one.
I think you’re letting the perfect be the enemy of the good, here. There are plenty of situations where you don’t need a robot to behave perfectly. People don’t behave perfectly.
No, it doesn’t work in this context because false positive is worse than nothing. False negative is better than nothing. Zero sum. Obviously it depends where you set the threshold of false positive and false negative. I imagined a very simple scenario the first time.
If even only .001% of the time, you’re going to cause a shit load accidents. You’re going to average a car slamming on the breaks for no reason like every… 2 minutes would be .12, 20 would be 1.2, 200 would be 12% 800 would be 48%, so you’re going to have every car slam on their breaks every 12-15 hours of drive time. That would be an absolute mess.
I have no idea what you’re thinking the scenario is here. The alternative is an uncontrolled car, I think I’d rather it had at least some brains behind the decisions it’s making.
How does it decide the car is uncontrolled? That’s a failure scenario, too.
I’m not even sure what you’re arguing. I said from the get go that there are niche cases where AI is nothing but positive. You seem to be arguing that there are a bunch more cases. Fine. Maybe the niche is slightly less thin and narrow than I think. Cool.
Facedeer is just a pro-AI concern troll from Reddit.
He kicked off his part of the thread by complaining about people, and then speculating that maybe this chip could do a thing without any evidence.
When the regular controller of the car - be it human, another AI, whatever - isn’t sending control signals, then the onboard controller knows that the car is uncontrolled. Of course it’s a “failure scenario”, I’m suggesting that this chip would be ideal for picking up when that sort of thing happens. The alternative is to just fall over.
I, too, am not sure what you’re arguing. I suggested that a low-power high-speed AI chip like this would be ideal for putting in robots, which have power constraints and aren’t always in reliable contact with outside controllers. That’s a very broad “niche” indeed. I don’t know what all this landmine stuff or probabilities of brake-slamming is all about or how it relates to what I suggested.
fpgas can sort of be a middle ground, but i don’t know if they’re capable of running llms
Is there such a thing as modular fpga so that you could “plug in” another one and add more gates, sort of daisy chain them? I don’t know if such interfaces exist , sounds like it might need lots of bandwidth.
I know very little about fpgas, so I can’t answer your question, but let’s hope someone else can