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This 16.2 inch M1 Pro MacBook Pro stands out as a sturdy workstation laptop with strong memory bandwidth, a sharp screen, and support for multiple external displays. The 32GB unified memory and 512GB SSD make it appealing for local AI tinkering and heavier multitasking, especially for larger models that need more memory. The main tradeoff is speed, since RTX GPUs are generally much faster for smaller and mid sized local models, and the machine is notably heavy at about 4.5 pounds plus charger. Battery health in the listing is also a concern, with a minimum of 70 percent and some arrivals around the low 80s.
If you're doing local AI experiments with smaller models (around 8B class) and want decent local inference without paying for a much higher-end machine, this is a good match because commenters report M1 Pro-class performance in that range (e.g., Q4 8B) and caution that very large models like 70B shouldn't be run on slower setups.
If you're trying to keep costs down while still getting enough unified memory for practical local experimentation, this is a better fit than lower-RAM options because commenters note that with higher unified memory (like 64GB) you can load bigger models, implying 32GB is the minimum tier for useful local work.
If you want to set up a Mac workstation with four external screens but still need some portability, this MacBook Pro is a strong fit because M‑Pro chips support 4 external connections and the older model can cost a lot less while still covering that workflow.
If you care about a high-quality display and can tolerate the weight, this is a good buy because one owner says the screen size/resolution/graphics quality are outstanding even though it's heavy (about 4.5 lb plus charger).
If you're buying for local LLM use where you'll run your own models (not relying on preinstalled AI features), this can work well because commenters say there's no AI preinstalled and the point is running your own local AI models on the machine.
If your main goal is fast local AI inference for small/mid LLMs, this may disappoint because commenters say RTX GPUs are usually much faster for small/mid models and that the MacBook Pro can be much slower for AI even if it can run larger models.
If you're sensitive to portability or don't want a heavy laptop to carry around, this isn't ideal because owners warn it's heavy (around 4.5 lb plus the charger) and that it will take a toll.
If you need the best battery health guarantee from a refurb listing, this may be risky because commenters flag that the description's "minimum 70%" battery health is lower than the ~85% they usually see on refurbished Macs.
If you're specifically trying to avoid Apple Intelligence/locked-down behavior, this may not be ideal because commenters say Apple Intelligence is installed on machines, can be disabled, but may get reactivated or lead to a locked-down feature set if you opt out.
If you plan to run very large local models (e.g., ~70B), this is not ideal because commenters explicitly caution not to run slow setups like this for very large models.
It can run local models, especially larger ones that need unified memory, but it is slower than recent RTX GPUs for small and mid-size LLMs. The M1 Pro's 32GB RAM is useful for experimentation, while 64GB unified memory is better for loading bigger models.
The listing's full description says the minimum battery health is 70%, which is lower than the roughly 85% often seen on refurbished Macs. One received unit was at 83% battery health, so expect condition to vary within that floor.
It is heavy at about 4.5 lb plus the charger, and several comments describe it as noticeably weighty. The tradeoff is the large 16.2-inch screen, which is praised for its size and display quality.
M-Pro chips are described as supporting four external connections, making this model a strong choice for a Mac workstation with multiple screens. That also leaves it portable enough to use away from the desk.
I just bought an rtx 5060 16gb $450. (Old aurora r8 so upgrade options are very limited coming from a 2070 8gb- I can sell it lol)
This laptop is great but lot slower for AI or I would be all over this. Can run larger models but much slower. Good deal though IMO if you need a big laptop. Beware it is heavy 4.5lb plus charger will take a toll on you for sure
I just bought an rtx 5060 16gb $450. (Old aurora r8 so upgrade options are very limited coming from a 2070 8gb- I can sell it lol)This laptop is great but lot slower for AI or I would be all over this. Can run larger models but much slower. Good deal though IMO if you need a big laptop. Beware it is heavy 4.5lb plus charger will take a toll on you for sure
Can AI be removed manually? I just don't want it on this
Can AI be removed manually? I just don't want it on this
There is none pre installed on it, he's talking about running your own AI models locally, on the machine. Newer Apple M series chips are faster for local AI applications
There is none pre installed on it, he's talking about running your own AI models locally, on the machine. Newer Apple M series chips are faster for local AI applications
There are some Apple Intelligence built into the macOs but it's not that smart and you can turn it off.
As for faster, depending on the M series's GPU but most of the time the recent RTX cards will be much faster. Apple has unify memory and can run larger models more often but small/mid LLM size will be much faster on RTX.
My old M1 Max with 24GPU was slower for Ollama running a 5B parameter models vs. RTX 2070. My current M4 Pro with newer 16 GPU cores still can't beat RTX 2070 (didn't try MLX but normal use for LLM the older generation of RTX cards are still much much faster.
lots of way to slice it though as there are diff factors.
If you want to set yourself up with a Mac workstation with four screens, and also have an option of portability, this is one of the best ways to do it. M-Pro chips apparently all support 4 external connections . I'm using a more recent model for this - but this older model would have been fine and costs a lot less.
Waiting for an M2 or better with 48 GB or more. I bought an open‑box M1 with 16 GB RAM, 16‑inch screen for $1,200 last year at BEstBuy, and I'm hooked. It truly feels like a laptop in most respects for me.
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Jan 1, 2026 3:06 AM
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Guess I won't be needed that RTX 5060 16GB that is coming.
Power faulted on the PC, I figured I will really troubleshoot it properly since it was some weird power thing. Followed some basic troubleshoot thing and the power supply was clicking and clicking, tripping basically.
Took off power on the Mb etc. to rest, still no go so it's def power supply...
Decided to go outside for the last test since I thought it would be fun and it was!!!
Fired it up-- then I saw this.
All had stock components for many years) but I was stressing the hell out of RTX 2070 that came with the machine originally (that's why I wanted to get RTX 5060)
I will rent GPU online for a while I guess.
Quote
from Elon69
:
There are some Apple Intelligence built into the macOs but it's not that smart and you can turn it off.
As for faster, depending on the M series's GPU but most of the time the recent RTX cards will be much faster. Apple has unify memory and can run larger models more often but small/mid LLM size will be much faster on RTX.
My old M1 Max with 24GPU was slower for Ollama running a 5B parameter models vs. RTX 2070. My current M4 Pro with newer 16 GPU cores still can't beat RTX 2070 (didn't try MLX but normal use for LLM the older generation of RTX cards are still much much faster.
lots of way to slice it though as there are diff factors.
AI too hard on my old pc.jpeg - 508497
Last edited by Elon69 January 1, 2026 at 09:29 AM.
Community Reviews
This 16.2 inch M1 Pro MacBook Pro stands out as a sturdy workstation laptop with strong memory bandwidth, a sharp screen, and support for multiple external displays. The 32GB unified memory and 512GB SSD make it appealing for local AI tinkering and heavier multitasking, especially for larger models that need more memory. The main tradeoff is speed, since RTX GPUs are generally much faster for smaller and mid sized local models, and the machine is notably heavy at about 4.5 pounds plus charger. Battery health in the listing is also a concern, with a minimum of 70 percent and some arrivals around the low 80s.
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This laptop is great but lot slower for AI or I would be all over this. Can run larger models but much slower. Good deal though IMO if you need a big laptop. Beware it is heavy 4.5lb plus charger will take a toll on you for sure
As for faster, depending on the M series's GPU but most of the time the recent RTX cards will be much faster. Apple has unify memory and can run larger models more often but small/mid LLM size will be much faster on RTX.
My old M1 Max with 24GPU was slower for Ollama running a 5B parameter models vs. RTX 2070. My current M4 Pro with newer 16 GPU cores still can't beat RTX 2070 (didn't try MLX but normal use for LLM the older generation of RTX cards are still much much faster.
lots of way to slice it though as there are diff factors.
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Power faulted on the PC, I figured I will really troubleshoot it properly since it was some weird power thing. Followed some basic troubleshoot thing and the power supply was clicking and clicking, tripping basically.
Took off power on the Mb etc. to rest, still no go so it's def power supply...
Decided to go outside for the last test since I thought it would be fun and it was!!!
Fired it up-- then I saw this.
All had stock components for many years) but I was stressing the hell out of RTX 2070 that came with the machine originally (that's why I wanted to get RTX 5060)
I will rent GPU online for a while I guess.
As for faster, depending on the M series's GPU but most of the time the recent RTX cards will be much faster. Apple has unify memory and can run larger models more often but small/mid LLM size will be much faster on RTX.
My old M1 Max with 24GPU was slower for Ollama running a 5B parameter models vs. RTX 2070. My current M4 Pro with newer 16 GPU cores still can't beat RTX 2070 (didn't try MLX but normal use for LLM the older generation of RTX cards are still much much faster.
lots of way to slice it though as there are diff factors.
you can run local ai model up to 64gb (vram)
you can run local ai model up to 64gb (vram)
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