191–200 of 349 posts
So happy with my base M4 purchase for $499 when it released. I even snagged a 1TB replacement drive for $199 a year ago. Feels like I got the last train out.
I got a 32gb M4 macbook air with the lower CPU core count, right before things got stupid. Can't even buy that skew now.
So happy with my base M4 purchase for $499 when it released. I even snagged a 1TB replacement drive for $199 a year ago. Feels like I got the last train out.
I feel that way about my GPU for normal gaming use. Now I’m afraid of it dying on me.
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Apple does this thing where they pick a very old game and then boast about how well it runs. It's so bizarre. Not like Mac gaming is good anyway...they have "Game Porting Toolkit" but it's not free and open like Proton so nobody will use it unless Apple pushes them. Removing Rosetta doesn't help either.
In fairness, Cyberpunk 2077 might be 5 years old but it is the modern day "Can it run Crysis". It is absolutely still a hard game to run.
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A $5 VPS is easy to block. It's way harder to block a stock mac mini from a residential IP address
But why a Mac mini over a $30 raspberry pi on the same residential connection?
So happy with my base M4 purchase for $499 when it released. I even snagged a 1TB replacement drive for $199 a year ago. Feels like I got the last train out.
My son is using my old ~2011 Mac mini with Linux Mint on it. Slow but works.
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Afaik most openclaw users aren’t running their llm locally - just using a mac mini as an always-on sandbox for agents.
A sandbox with access to iMessage. Can't have your agent texting with green boxes.
So happy with my base M4 purchase for $499 when it released. I even snagged a 1TB replacement drive for $199 a year ago. Feels like I got the last train out.
Wait, their website only shows preorders for M6 or M5 Pro, and they have nothing about the M4. So do they currently have zero Mac minis in stock?
2.5Gbe as standard is nice.
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Don't worry, there are exactly zero Apple chips that are good for any sort of local inference right now. Unified ram is fast for ram, but dogshit slow compared to actual dedicated VRAM on graphics cards.
Your statement is not really true any more. I have an old M1 Max with 32GB that runs inference models fine, especially those using MLX. Dedicated graphics cards are faster but not to the point that it matters. That’s a six year old machine. My newer machine M5 max 128GB will far outperform your typical 32GB gaming card once the model exceeds memory. To even get close to that on a dedicated graphics card you are payin…
It definitely matters, if you're intending to run a Claude Code/OpenCode style agent workflow. Most of those harnesses start with 8-12k token contexts, which is a lot of prefill for a Mac but cheap for a CUDA GPU. The GPGPU compute on the fastest Macs is still trailing behind Nvidia's laptop GPUs; the highest-bandwidth Apple Silicon chip (now the M5 Ultra) has ~7x lower memory bandwidth than a single B100 card.
There's a good reason why Apple Silicon isn't to be found anywhere in the datacenter buildout. It's nonviable for training, and wastes electricity running real-world inference workloads.