Once you've used a model that runs at hundreds of TPS, it's hard to go back. Everything completes so quickly that you can iterate without breaking out of flow state. My biggest gripe with slow (<50tps) LLMs is that I've lost all the mental context I built up by the time it's done, which makes it extremely difficult to explore or iterate on solutions.

> Once you've finished reading the next article on HN about a 5 tps Xeon, your task will be complete.

If I spend 10 minutes reading an article, that would only generate 3000 tokens.

That’s not counting the prompt processing time.

We have very different expectations for LLMs if your tasks only take a couple thousand tokens and you’re happy waiting 10 minutes for it.

> Yes, with top-tier GPU farms you can hit hundreds of tokens per second

My 5090 gets hundreds of tokens per second with this model. No farm needed. I’d have to double check but I think even a $1000 Intel B70 might break 100 tokens per second.

> But if the old Xeon in the closet can get useful work done at 5 tokens per second, there are lots of people and lots of use cases where a free, unlimited 5 TPS stream is worth more than paying a dollars per day to get access to a 500 TPS source.

If that old Xeon pulls 200W from the wall and you pay national average electricity costs, it’s going to cost $0.90 per day to run it.

I would rather pay a dollar per day, get my answers 100X faster, and not have an old Xeon heating up my house.

We clearly have different goals. I want an LLM to review my code, not the other way around.

We aren’t there yet. Not for frontier development work at least.

is there a good tool to manage these workloads? batch process a bunch, handle failures, retry things etc?

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