I'm not paying $20 for ChatGPT or Claude because a free local LLM does
xda-developers.com29 points by hsnewman 2 hours ago
29 points by hsnewman 2 hours ago
If you were only paying $20 for LLMs previously then you were getting very low usage / little done.
Ok. What can I do with an M4 MBP, with 48G memory?
Thanks for writing and sharing. Since I also have a 4070 Ti Super, I'm always interested in hearing people's experiences with local models that fit 'middle-ground' hardware. I kind of feel left out because most posts I see seem to either be about clever ways of making older, lower-end cards usable, leveraging mega-CPU RAM (128GB) or how aweseome high-end GPUs like 4090/5090 can be.
> llama.cpp serves it over localhost at speeds that stop being a complaint after a few minutes of use.
"That stop being a complaint"? That's a strange construction.
I wonder if qwen 4 will make these smaller cards more viable by allowing the ngram storage to be hosted on CPU RAM.
20$/month vs. 1200$ gpu. That's a lot of months you can pay the subscription
A $1200 GPU you already own, typically, is how this is being seen. Maybe you already have a gaming PC, so this is 0 extra cost to you. All it takes is the power to run the GPU which would be minimal extra vs. the $20/mo cost.
Where I live electricity costs 30 to 40 cents per kWh, I think it's easy to go over 20 dollars per month. Also I find 20 dollars is really a small amount of money to spend on AI. AI fays itself back fast if it helps you with serious stuff.
A lot of quotas, reset timers, changed terms, and so on too through the subscription. I prefer to buy and use my tools, not adopt them as part of my lifestyle.
I bought my 4070 Ti Super in that magic ~month right in between the Crypto-Craze and the AI Bubble when good cards were pretty commonly available for around MSRP. I even found a small deal the day I was ready to buy, so paid $790 with 2-day delivery included.
Throughout the entire crypto-craze I'd been squeeking by gaming on an OC'd 1080 Ti, so when prices finally fell I was more than ready. Since I was getting interested in maybe playing around with AI soon, I spent up from my budget of around $550-$600 and am very glad I did since it's now clear I'll be squeeking by on this card for more years than I'd planned, just like the 1080 Ti.
This is a great article! Thank you for sharing. I also recommend opencode that has built in features for local model hosting.
"I spent $1100 on a graphics card so I can save $20/mo running a mid/low intelligence model at 33tk/s with 16k context"