Sorry, but what are you talking about? This is a 120B-A6B model, which isn't runnable on any laptop except the most beefed up Macbooks, and then will certainly drain its battery and cook your legs.
Sorry, but what are you talking about? This is a 120B-A6B model, which isn't runnable on any laptop except the most beefed up Macbooks, and then will certainly drain its battery and cook your legs.
Yeah my bad, it requires an expensive MacBook.
I think it would still be fine for the legs and on battery for relatively short loads: https://www.notebookcheck.net/Apple-MacBook-Pro-M5-2025-revi...
But 40 degrees and 30W of heat is a bit more than comfortable if you run the agent continuously.
You can easily run a quant of this on a DGX Spark though. Seems like a small investment if it meaningful improves Lean productivity.
Is it though?
Most people I know that use agents for building software and tried to switch to local development, every single time they switch back to Claude/codex.
It's just not worth it. The models are that much better and continue to get released / improve.
And it's much cheaper unless you're doing like 24/7 stuff.
Even on the $200/m plan, that's cheaper than buying a $3k dgx or $5k m4 max with enough ram.
Not to mention you can no longer use your laptop as a laptop as the power draw drains it - you'd need to host separately and connect
A single DGX Spark can service a whole department of mathematicians (or programmers), and you can cluster up to 4 of them them to fit very large models like GLM-5 and quants of Kimi K2.5. This is nearing frontier-level model size.
I understand the value proposition of the frontier cloud models, but we're not as far off from self-hosting as you think, and it's becoming more viable for domain-specific models.
That's great news- I wonder if that will help drive cloud costs down too