Comment by satvikpendem
3 days ago
> I'm curious what the mental calculus was that a $5k laptop would competitively benchmark against SOTA models for the next 5 years was.
Well, the hardware remains the same but local models get better and more efficient, so I don't think there is much difference between paying 5k for online models over 5 years vs getting a laptop (and well, you'll need a laptop anyway, so why not just get a good enough one to run local models in the first place?).
Even if intelligence scaling stays equal, you'll lose out on speed. A sota model pumping 200 tk/s is going to be impossible to ignore with a 4 year old laptop choking itself at 3 tk/s.
Even still, right now is when the first gen of pure LLM focused design chipsets are getting into data centers.
> Even if intelligence scaling stays equal, you'll lose out on speed. A sota model pumping 200 tk/s is going to be impossible to ignore with a 4 year old laptop choking itself at 3 tk/s.
Unless you're YOLOing it, you can review only at a certain speed, and for a certain number of hours a day.
The only tokens/s you need is one that can keep you busy, and I expect that even a slow 5token/sec model utilised 60s in every minute, 60m of every hour and 24 hours of every day is way more than you can review in a single working day.
The goal we should be moving towards is longer-running tasks, not quicker responses, because if I can schedule 30 tasks to my local LLm before bed, then wake up in the morning and schedule a different 30, and only then start reviewing, then I will spend the whole day just reviewing while the LLM is generating code for tomorrow's review. And for this workflow a local model running 5 tokens/s is sufficient.
If you're working serially, i.e. ask the LLM to do something, then review what it gave you, then ask it to do the next thing, then sure, you need as many tokens per second as possible.
Personally, I want to move to long-running tasks and not have to babysit the thing all day, checking in at 5m intervals.
At a certain point, tokens per second stop mattering because the time to review stays constant. Whether it shits out 200 tokens a second versus 20, it doesn't much matter if you need to review the code that does come out.
If you have inference running on this new 128GB RAM Mac, wouldn't you still need another separate machine to do the manual work (like running IDE, browsers, toolchains, builders/bundlers etc.)? I can not imagine you will have any meaningful RAM available after LLM models are running.
No? First of all you can limit how much of the unified RAM goes into VRAM, and second, many applications don't need that much RAM. Even if you put 108 GB to VRAM and 16 to applications, you'll be fine.
How about the rest of the resources? CPU/GPU? Would your work not be affected by inference running?
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