Comment by LeifCarrotson
5 hours ago
I think that any workflow that requires the user to stare at the tokens being generated live is using it wrong. Delegate, don't stare!
https://mikeveerman.github.io/tokenspeed/?rate=10&mode=text
You think of an idea that you want to have the LLM process, queue it up, and go back to what you were doing. Once you've finished reading the next article on HN about a 5 tps Xeon, your task will be complete. It's kind of like using a 3D printer: It doesn't matter if a print takes 10 hours, because when you come back in the morning it will be done.
Yes, with top-tier GPU farms you can hit hundreds of 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.
> It's kind of like using a 3D printer: It doesn't matter if a print takes 10 hours, because when you come back in the morning it will be done.
This is how I used to think about my 3D printer, but FWIW the way my actual thinking and planning works, print speed really matters. Not for the final print, but for iterative work and test parts, it is obvious that either having a fast printer helps. Having multiple slow printers also helps, but there are only so many areas of a design you can iterate on at once.
At the moment my own LLM use is experimental and iterative, and I definitely favour the faster MoE models for much of what I am doing, even if I might in principle prefer to get the final work done in the slower ones.
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.
I'd rather have slower and better output than worse and faster output.
In 1980s ibm has studied and said why sub-second response needed to maintain the mental flow. That time you send a whole screen unlike unix like character by character. This proves very true even when you deal with form processing. I think that we are dealing with the same issue here.
Keep your mental context in your brain is critical
> 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.
You could suspend it to ram, and only wake it up on request, it takes 2 seconds on my box.
It’s not a cost savings relative to paying API prices even if you’re suspending it.
This is an option if you must run local inference, you’re not sensitive to speed, and the budget is low.
It’s not going to be cheaper than paying API prices for the model though.
We clearly have different goals. I want an LLM to review my code, not the other way around.
I'm sure this exact topic has been argued hundreds of times already on HN, but I think I have a new "possibly agreeable to both sides" perspective on this after having lost man-years to retired corporate code aka "FAIAP, throwaway code"
Let LLMs write the corpo code, as it will be unlikely to still be running in 5-10 years. Frontier AI is already at the point where it writes fewer bugs per LOC than humans. By a lot.
Go ahead and do your bespoke coding on your side-project loves and core libraries... The stuff that will last, anyway.
But if you're working for a corpo and still doing bespoke... That's... not gonna last, I'm afraid. Well, either you remaining there, or that, as it were.
There's a whole spectrum of employment between faceless corporations and personal side projects. AI will replace humans because giant business believe they can do the same work, not because they will actually be able to.
The correctness of an application is limited by your ability to understand and describe what you need. We have a word for an application specification tool so detailed it eliminates all ambiguity. It's called a "programming language".
The mistakes are always in the transfer from human to machine. I still find a high-level programming language to be the best way to express my intent. Humans will make mistakes in the hand-off to AI just like they make mistakes in the hand-off to code, but at least code is deterministic.
It's still the same thing, you can ask it to do a full on report give explanation and details be thorough and then go do something else, another task a lunch break whatever and it will be done when you're back
> another task a lunch break whatever and it will be done when you're back
At 5 tokens per second and unknown prompt processing speed, you may need a very extra long lunch break depending on your codebase.
How do you maintain a flow state during a lunch break? I'm looping with Claude on a scale of minutes. While you're waiting, I'm iterating.
6 replies →
We aren’t there yet. Not for frontier development work at least.
Except often queued agentic flows must be checked in on. Or to use the comparison, 3D printers are not immune to making spaghetti all night when something goes wrong. (I’m not a 3d printing expert so maybe that is solved now)
It is common for agents to just stop because overload or some API error hijinks.
Or you get a TUI question that is blocking.
In general you’re right though, staring at tokens from agentic is not time well spent.
Some of these I’ve built custom harness around in iterm2 though.
is there a good tool to manage these workloads? batch process a bunch, handle failures, retry things etc?
Filament snaps at 1am and then you have to run print again. 10 hours turn into many days potentially.
I watch tokens to see if it goes in right direction. If model goes off the rails, then it is time to stop and adjust prompt.
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