tehnomad

u/tehnomad@lemm.ee
1 posts · 22 comments

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I found a VRAM calculator for LLMs here: https://huggingface.co/spaces/NyxKrage/LLM-Model-VRAM-Calculator

Wow it seems like for 128K context size you do need a lot of VRAM (~55 GB). Qwen 72B will take up ~39 GB so you would either need 4x 24GB Nvidia cards or the Mac Pro 192 GB RAM. Probably the cheapest option would be to deploy GPU instances on a service like Runpod. I think you would have to do a lot of processing before you get to the breakeven point of your own machine.

The context cache doesn't take up too much memory compared to the model. The main benefit of having a lot of VRAM is that you can run larger models. I think you're better off buying a 24 GB Nvidia card from a cost and performance standpoint.

I would suggest an Intel N100 mini PC if you are planning to transcode video files with Plex. Intel Quick Sync performs better than AMD for media transcoding.

I wasn't sure if it was AI or not. According to the description on GitHub:

Utilizes state-of-the-art algorithms to identify duplicates with precision based on hashing values and FAISS Vector Database using ResNet152.

Isn't ResNet152 a neural network model? I was careful to say neural network instead of AI or machine learning.

I always used fingertip with small hands and never thought it was weird. I just figured out maybe it's because I played piano growing up? It feels more natural to have a C shape with my hand and space between the mouse and my palm.

Yeah, the power prices in my city are really high (USA). They're even higher than Hawaii, from what I've heard. That's why I'm leaning towards the mini PCs and SBC options, even if used server/desktop parts have better performance for the price.