Struggling with the performance and usability of AI models on personal hardware

Signals over time

2 observed signals across 1 sources, tracked for 1 days. Confidence: low.

What people are saying Observed

• “Most responses here are along the lines of "model capabilites move too fast to build hardware for". I think the fact that there are plenty of 1yr+ old models on openrouter serving hundreds of billions of tokens a month shows that there's plenty of use case for models that are "good enough. Cerebras' entire business is serving older models at high speed. I would happily use an opus 4.7 at 15k tokens per second. The intelligence per second of an ASIC still makes sense even with rapidly evolving models.”

  Hacker News · wishlist

• “i did in fact test different Qwen's! :) qwen3.5:9b and qwen2.5-coder:14b, i think. but i struggled with speed and them being stuck in "Hmm, wait, no, that doesn't work, let me analyze again"-loops. on the M4 Air I was able to start some bigger models. definitely the first time i burned through 25% of battery in half an hour while getting first-degree burns on my lap. maybe in 1-2 years my hardware will be enough for fast local-AI. maybe by then i can afford new hardware. (think with current trends option #2 gets less realistic each quarter). not sure if i lost a bit of curiosity and spark. there's so many models, tools, harnesses, tweaks for harnesses, edited models and so many things. ultimately i don't care enough, it seems vapid to stay bleeding-edge informed about LLMs, if one's career does not directly depend on it. i mean, what the fuck even is a bonsai quant, this stuff makes me feel like an uninformed excel boomer even tho i absolutely don't am one :D not a software engineer per se so i don't need 16 agents running 24/7 with openclaw. i want a local buddy who helps me write ansible/python faster, better, helps me analyz”

  Hacker News · frustration