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Comment by bitexploder | Hacker News Reader

It doesn’t. Decode is by far the most dominant and expensive part of agentic tasks and once prefill hits a certain achievable speed decode dominates the time in ant agentic flows with reasoning. There is a reason input is so much cheaper :)

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Isn’t input so much cheaper because it’s just less work? Prefill is usually 10-20x as fast as generation.

If you have 200k context, but only 200t/s prefill, then you’ll wait multiple minutes when continuing an average session from yesterday. Or compaction - might take 15 minutes.

No commercial service has 200 t/s prefill though. It is 1000s of pps for prefill. It is largely compute bound and very very fast on modern GPUs and model architectures. That is my point. It is an order of magnitude faster than decode, which is largely bound by memory bandwidth and just has a much harder limit. Prefill tends to look like a small slice in typical compute / token generation budgets. I am not saying it isn't important (I have been working hard on my own inference engine for flash next and am intimately familiar). I am saying it does not dominate. Also compaction and reading the whole prompt tends to be one of the few times you really hit peak pps because it is cached and you can just fill your data pipes for a bit. Also Deepseek showed you can make kv cache really cheap. Decode is still decode.

And what makes decode so memory intensive and seemingly unhackable like the other steps

The math. It’s pretty brutal. You have to multiply insanely large matrices in memory. A dense model has to have and multiply across every parameter basically. MoE models work around it, sure, but you still have to hold a lot in memory and density of activated parameters matters. There is not much of a way around it yet. We are potentially getting there, but its not obvious yet.

And scaling intelligence is seemingly only achievable by making these matrices larger?

What makes an etched LLM into silicon better at big matrix multiplication

Where does quantum fit into this

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