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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.
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.
For 8B Q4 llm AI,
M1 Pro, M5 gets 30 ts $800
M1 Mac gets 60 ts $1200
M4 Max gets 90 ts $2400
5060 Mobile gets 50 ts $600
Of course 5060/5070 Desktop is faster but burns power 100-200 ts
MLX MoE is alot faster. I wouldn't run slow 70B
For 8B Q4 llm AI,
M1 Pro, M5 gets 30 ts $800
M1 Mac gets 60 ts $1200
M4 Max gets 90 ts $2400
5060 Mobile gets 50 ts $600
Of course 5060/5070 Desktop is faster but burns power 100-200 ts
MLX MoE is alot faster. I wouldn't run slow 70B
thanks for this! I do need to try MLX but I don't like LM Studio as much. It should try it.
I am now looking for cheap Z440 HP box to put my ram, rtx 2070 in as a stop gap of my computer that caught on fire.
Will still look to see if I can find a cheap Mac machine with larger ram and good enough inference. the new open source models are looking great and I will try them again instead of just the online versions.
Mine arrived. Laptop was in great physical condition. Battery health was 83% which is... ok.
FYI the ebay listing title has product code "MK1H3LL/A A2485 LB" in the title which is the MBP with M1 Max, but the actual product code is "MK1E3LL/A" with M1 Pro as shown in the listing.
For 8B Q4 llm AI,
M1 Pro, M5 gets 30 ts $800
M1 Mac gets 60 ts $1200
M4 Max gets 90 ts $2400
5060 Mobile gets 50 ts $600
Of course 5060/5070 Desktop is faster but burns power 100-200 ts
MLX MoE is alot faster. I wouldn't run slow 70B
you can load bigger models with 64gb unified memory, many consumer gpus can't do the same
thanks for this! I do need to try MLX but I don't like LM Studio as much. It should try it.
I am now looking for cheap Z440 HP box to put my ram, rtx 2070 in as a stop gap of my computer that caught on fire.
Will still look to see if I can find a cheap Mac machine with larger ram and good enough inference. the new open source models are looking great and I will try them again instead of just the online versions.
The cheapest way to play around with all open source LLM is using groq, cs, aws, tpu asic accelerators with 500 ts - 1500 ts with unlimited vram and low latency 100ms. That gets real work done.
You can run several 500B Gwen, Deepseek r1, oss 120B 2tb vram for pennies instead of spending $2k-$50k get 1/10th speed, spend 1000x more
The tiny 7B llm are useless. 32B and above are useful, but requires at least 16gb-32gb vram to run good.
Slow charge (30W-65W) to 80% auto setting ON, and your battery will last lifetime. I don't use the 140W charger much.
I did this to 4 year old laptop battery, its still has 99% battery health when kept at 30%-80% battery meter.
Getting OEM Apple batteries is pain in the ass when off warranty
you can load bigger models with 64gb unified memory, many consumer gpus can't do the same
With $600 3090 24gb vram, you can offload to 64gb cpu system ram getting a snail 2-5 ts while burning 300-450 watts
64gb unified ram only needs 30-60 watts for 5-10 ts. For 48GB-56GB models.
And use cloud to biggest llm run the 60GB to 500GB llm for $1/1M tokens for 500+ ts
Unified Memory & buying 1M tokens is the most insane value for multi-use
3090 is great choice if you already have gaming rig and need a space heater in winter
Last edited by FairCable4794 January 12, 2026 at 10:57 AM.
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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.
Common Questions (4)
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.
AI-generated summary based on comments from the community. Updated ago
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24 Comments
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from ebay, you just need to keep monitoring those listings, they were sold out within a few hours
$1300 for 100% battery like new with warranty
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.
M1 Pro, M5 gets 30 ts $800
M1 Mac gets 60 ts $1200
M4 Max gets 90 ts $2400
5060 Mobile gets 50 ts $600
Of course 5060/5070 Desktop is faster but burns power 100-200 ts
MLX MoE is alot faster. I wouldn't run slow 70B
M1 Pro, M5 gets 30 ts $800
M1 Mac gets 60 ts $1200
M4 Max gets 90 ts $2400
5060 Mobile gets 50 ts $600
Of course 5060/5070 Desktop is faster but burns power 100-200 ts
MLX MoE is alot faster. I wouldn't run slow 70B
I am now looking for cheap Z440 HP box to put my ram, rtx 2070 in as a stop gap of my computer that caught on fire.
Will still look to see if I can find a cheap Mac machine with larger ram and good enough inference. the new open source models are looking great and I will try them again instead of just the online versions.
Mine arrived. Laptop was in great physical condition. Battery health was 83% which is... ok.
FYI the ebay listing title has product code "MK1H3LL/A A2485 LB" in the title which is the MBP with M1 Max, but the actual product code is "MK1E3LL/A" with M1 Pro as shown in the listing.
$1300 for 100% battery like new with warranty
M1 Pro, M5 gets 30 ts $800
M1 Mac gets 60 ts $1200
M4 Max gets 90 ts $2400
5060 Mobile gets 50 ts $600
Of course 5060/5070 Desktop is faster but burns power 100-200 ts
MLX MoE is alot faster. I wouldn't run slow 70B
thanks for this! I do need to try MLX but I don't like LM Studio as much. It should try it.
I am now looking for cheap Z440 HP box to put my ram, rtx 2070 in as a stop gap of my computer that caught on fire.
Will still look to see if I can find a cheap Mac machine with larger ram and good enough inference. the new open source models are looking great and I will try them again instead of just the online versions.
You can run several 500B Gwen, Deepseek r1, oss 120B 2tb vram for pennies instead of spending $2k-$50k get 1/10th speed, spend 1000x more
The tiny 7B llm are useless. 32B and above are useful, but requires at least 16gb-32gb vram to run good.
Locally Light AI PC, 32gb Intel 258v ($400) 20 ts, 32gb M1 Pro ($700) 30 ts, 24gb M4 ($800) 25 ts
Mid AI PC, 64gb M1 Max ($900+) 60 ts, 3090 24gb ($600+) 100 ts, 64gb-128gb AMD 395 ($1300+) 80 ts
High End AI PC, $4k+ DGX Spark 128gb, Apple M4 Max 128gb , 6000 96gb, M3 Ultra $10k isn't worth it unless your company paying for it
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mine is 89%
I did this to 4 year old laptop battery, its still has 99% battery health when kept at 30%-80% battery meter.
Getting OEM Apple batteries is pain in the ass when off warranty
you can load bigger models with 64gb unified memory, many consumer gpus can't do the same
64gb unified ram only needs 30-60 watts for 5-10 ts. For 48GB-56GB models.
And use cloud to biggest llm run the 60GB to 500GB llm for $1/1M tokens for 500+ ts
Unified Memory & buying 1M tokens is the most insane value for multi-use
3090 is great choice if you already have gaming rig and need a space heater in winter
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