It’s hot and also LOUD and runs the battery down quick.
But I’m having a lot of luck just running things when I’m away from the computer and can leave it plugged in.
It starts going weird (unreliable and slow) with context over 80k so you have to pick tasks one at a time and baby sit a lot more than Claude. But it really is very capable and feels like there’s an intelligence there to talk to. Maybe gpt-4 level clever?
I have an m5 max 64gb and I think anything slower would be quite painful.
There was a lovely window of a few years when processors were fast enough and low-power enough that real development work could trivially happen on a Macbook Air in a lounge.
That window is waning as more and more memory and graphics processing power is being used locally.
I suppose the future is most likely going to involve farming out AI requests to your desktop machine, your company's compute farm, or a cloud endpoint, but if you're expecting to run an AI offline on your lap with a battery...your lap will get warm.
No idea what you are talking about. My battery lasts longer than ever while running vim and make and GCC. It’s amazing. Not sure why your windows are closing.
Because the local LLM, which you are not running, is running for much longer than gcc and is eating the battery. Different choices, different outcomes.
Bur the implication here in the parent comment is that for ‘real dev work’ to be done you must be running an LLM, and therefore you can’t do it on a MacBook anymore.
Turns out you can still accomplish stuff with a text editor and compiler.
Of course we can code without a LLM, but if we want to, as the sibling comment says, don't run a local LLM on a laptop. I'm offloading the LLM to the LLM vendor, like most of us IMO. The CPU is still busier then it used to be, because of the local harness.
> There was a lovely window of a few years when processors were fast enough and low-power enough that real development work could trivially happen on a Macbook Air in a lounge.
I was responding to this. I am appalled that anyone thinks (and is willing to say out loud in public) that they cannot do "real dev work" without an LLM.
Merely existing noticeably ages most battery chemistries.
You can take half an hour out of every week inconveniencing yourself to protect your battery, or you can spend an hour once a year just putting in a new one (or having it done for you in-store if you're using an Apple device) and save yourself the nagging worry.
I have an m3 max 64GB and it runs ok, I was playing around with it last night. Its reasoning is better, but the token/s still makes an MoE model (35B A3B) preferable so I switched back.
Try using Goose instead of Claude's harness? Goose doesn't load as much context in at the start, so it might be more usable. I've definitely been able to get it past 128K, although I typically don't go beyond 70-80k. MoE might also be better at this.
If you're running it while idle and don't need the quickest results, reducing the clock speed improves energy efficiency (and in your case avoids overheating the battery). There will be some optimal speed that maximizes computations per joule that depends on the specific load and can only be found by measurement.
On Linux, you can cap CPU frequencies with "cpupower". Does MacOS have any equivalent?
My M5 Max has 3 modes, "low", "automatic", and "high". Automatic doesn't seem to simply switch between low and high -- it seems to sit in the middle and vary dynamically based on workload and temperature. With automatic I get more than half as many tokens per second as on high, but with much lower fan speed and temperature.
But I’m having a lot of luck just running things when I’m away from the computer and can leave it plugged in.
It starts going weird (unreliable and slow) with context over 80k so you have to pick tasks one at a time and baby sit a lot more than Claude. But it really is very capable and feels like there’s an intelligence there to talk to. Maybe gpt-4 level clever?
I have an m5 max 64gb and I think anything slower would be quite painful.