Yeah I found some stats now and indeed you’re gonna wait like an hour to process if you throw like 80-100k token into a powerful model. With APIs that kinda works instantly, not surprising but just to give a comparison. Bummer.

Thanks! Hadn’t thought of YouTube at all but it’s super helpful. I guess that’ll help me decide if the extra Ram is worth it considering that inference will be much slower if I don’t go NVIDIA.

Yeah I was thinking about running something like Code Qwen 72B which apparently requires 145GB Ram to run the full model. But if it’s super slow especially with large context and I can only run small models at acceptable speed anyway it may be worth going NVIDIA alone for CUDA.

Meh, ofc I don’t.

Thanks, that’s very helpful! Will look into that type of build

I understand what you’re saying but I’m coming to this community because I like having more input, hear about the experience of others and potentially learn about things I didn’t know about. I wouldn’t ask specifically in this community if I wouldn’t want to optimize my setup as much as I can.

Interesting, is there any kind of model you could run at reasonable speed?

I guess over time it could amortize but if the usability sucks that may make it not worth it. OTOH really don’t want to send my data to any company.

I’d honestly be open for that but would an AMD setup not take up a lot of space and consume lots of power / be loud?

It seems like in terms of price & speed, the Macs suck compared to other options, but if you don’t have a lot of space and don’t want to hear an airplane engine constantly I’m wondering if there are options.

Yeah the VRAM of Mac M series is very attractive for running models at full context length and the memory bandwidth is quite good for token generation compared to the price, power consumption and heat generation of NVidia GPUs.

Since I’ll have to put this in my kitchen/living room that’d be a big plus but idk how well prompt processing would work if I send over like 80k tokens.