Ask HN: MacBook vs. Dedicated GPU for LLM
24
mmzubairtahir about 3 hours ago 45 comments
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For those who are using llms on macbook, Want to understand how macbook is different than dedicated GPU in running those models? and how to know how much a macbook is capable of running a model?
Discussion (45 Comments)Read Original on HackerNews
On that note though, most car salesman are somewhat stupid, although the best ones are atleast normal. (I was a cal mechE grad and top 0.2% nationwide car salesman). They also slowly brainwash themselves into believing most of what they say.
I'm currently running those models using an RTX 5070 12GiB + RTX 5060 16GiB + RTX 3060 12GiB with a 96k context size with MTP/speculative decoding and I'm quite happy (the 5070 is about 4x faster than the 3060, the 5060 is inbetween them so about 2x faster than a 3060).
Open weight models are getting good. With GLM 5.2 now chasing Opus, I'm very excited to see a smaller model's distillation.
Plus, the OLED MacBook Pro should be released by then.
Competition and innovation will hopefully make the bubble pop, and we'll get reasonably priced local hardware to run very intelligent models. Something like Talaas with GLM 5.2 would be pretty cool. Or Apple printing the latest model onto hardware—it would give a new reason to buy a new Mac every year (a new ai model with every new version).
Works out to about 1.1exaflops of fp4. Networking is 800gbps.
120kW per rack.
Dedicated GPUs have less video RAM so can run smaller less smart models quickly.
With the model using MLX the speed increase is night and day. Even non-MLX is good.
You also don't have the transfer costs related to moving CPU data into the GPU.
Together with pi mono I wouldn't want to go back to Claude & Co. Speed, quality of the answers, short answer times at any time of day - once you have eaten from the fruit your definition of SOTA will change...
For reference, I do software development since 30 years, I am not vibe coding the umpteenth todo list.
But it really depends on what it is you want to do. An MLX optimised recent model will run fine and at decent speeds. Granite4.1 (a few months old) for example takes up 2GB of memory, insanely fast and results are good vs much bigger models like gpt-oss-120b (a year old). It even runs on an M1 mac with good speeds.
The models are only getting better.
You gotta really want it right now.
It's still early!
Also beware local models tend to be slow. Also, the main optimization trick for LLM inference is running large batches (concurrent users) and you won't take advantage of this (batch=1).
IMHO using Macs for LLMs is a fad. An expensive fad.
Dual 3090s are terrible airpods
If you don’t already have a MacBook, then there’s a bit of a sweet-spot for the AI experimenter right now, which is to buy a second-hand 16” MBP with an M1 Max chip and 64GB of shared ram. Because these are about 5 years old now, they have depreciated to the point where they can be had for around £1100 / €1300 / $1500 and make a phenomenal platform for learning because the 64Gb of shared memory means you can host models up to about 48GB in size, and then task them to do interesting things with coding without ever having to worry about token burn.
The downside is that they’re slow, and prone to having to be nudged to keep them on track, but that’s part of the fun too. The “latency” is atrocious granted - you ask something and the machine thinks for a few minutes before saying anything which is a different experience to using Claude. But… it does work. You can think of yourself more like a manager with a junior member of staff and set the machine running and leave it to do its thing for a couple of hours which can be actually useful work, but this approach will likely be shouted down by some commenters here who treat Claude like some kind of expensive and quick-fire dopamine pump. Can also use a Mac like this for running diffusion models for image generation and suchlike in ComfyUI, even though, again, results will be slow. Spending more money on a more recent MBP with as much RAM as you can afford will deliver the same results more expensively in a quicker and quicker time.
To get the same kind of size of model you’d have to combine a couple of Nvidia 3090 24GB cards in a decent workstation with the PCI capacity to handle them, or hack some kind of solution to hang GPUs off the back of a motherboard on ribbon cables with the GPUs running on their own PSU, which is what I’m building next… the difference is those cards have 24GB of vram and cost about $1000 each second-hand, but will operate much much faster than the M1 Max MBP, or even the most recent M5 because they have so much more bandwidth (because they’re burning 350 watts on GPU compute rather than 140 watts total which is what a super efficient MBP has for the cpu/gpu/screen/everything).
So say you had $6000 to spend today, you could buy a second hand workstation and craft a solution with external GPUs which would completely smoke any Mac in existence, even though macs have the edge in the size of model you’d can run (slowly) due to their shared memory. External GPUs and access to the Nvidia frameworks and general CUDA ecosystem wins out on the performance front though. A real sweet spot is to buy an M1 Max MBP and have that as your front end to a Linux workstation full of GPUs.
But any apple silicon MBP is a totally competent gateway drug to local agentic computing.
Google Gemini could give you an in-depth and useful discussion about this exact question.