Ask HN: MacBook vs. Dedicated GPU for LLM

https://news.ycombinator.com/item?id=48694802

by mzubairtahir • 26 days ago

37 71 26 days ago

news.ycombinator.com

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?

JSR_FDED2• 26 days ago

MacBooks with their unified memory behave like a slow GPU with enormous amount of video RAM. So you can run large smart models slowly.

Dedicated GPUs have less video RAM so can run smaller less smart models quickly.

exabrial• 26 days ago

Do Mac Pros provide more headroom? noob here, noob questions

rho138• 26 days ago

Idk why you’re being downvoted for asking a question. Pending specs they _could_ provide more headroom for a larger model but they would still be limited by the CPU and it’s associated bus speeds.

JSR_FDED2• 26 days ago

In what sense? Headroom for what?

zihotki2• 25 days ago

Not that much - a bit better but still negligible. Prefill is highly parallelizable and benefits from many cores. NVidia GPU's simply have several times more cores than even Pros.

mzubairtahir4• 26 days ago

how much memory is actually useable by gpu in macbook? as it is shared?

pylotlight• 26 days ago

roughly ~50–56GB, although this is somewhat configurable with iogpu.wired_limit_mb. By default, macOS reserves ~25% of memory for the system.

visarga• 26 days ago

Macbook M5 64GB - can run gemma-4-26b-a4b-it-4bit and Qwen3.6-35B-A3B-4bit at about 1500 tps prefix and 45 tps decode on contexts up to 100K tokens using MLX. It's faster than Claude. I was really surprised, chat quality is also similar to Claude for gemma4. Agentic works but does not compare to cloud models, you can still make agents where top level is code.

mzubairtahir4• 26 days ago

sorry but asking again: how much memory is actually useable by gpu in macbook? as it is shared(os and apps also have to use same memory)? and it is different than dedicated gpu memory?

EagnaIonat4• 26 days ago

MacBooks with their unified memory behave like a slow GPU with enormous amount of video RAM. So you can run large smart models slowly.

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.

emmettbt• 23 days ago

Unified memory is also a kicker for crashing a Mac.

epsteingpt• 26 days ago

Both are going to be super super slow and low payback.

You gotta really want it right now.

It's still early!

jpgvm• 26 days ago

If you want a massive MacBook anyway then it's great. They are decent for local LLMs, awesome for local image models and it's a MacBook so AppleCare+ has your back. IMO it's a no brainer if you wanted a MacBook anyway but it's a poor choice if your reason to buy it is to run LLMs.

mzubairtahir4• 26 days ago

are you saying because of speed or it just cant run them?

zepearl3• 26 days ago

I agree. To run an acceptable model (e.g. Qwen/Qwen3.6-27B or google/gemma-4-31B) with a good quantization (minimum Q5) with a good context size (min 64k) you could buy 2 or even 3 GTX 5060 16GiB VRAM for ~550$ each. Fyi the much faster MoE models were useless for my usecases - e.g not able to correctly identify me/I/you, endless thinking loops, etc.

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).

nubg• 26 days ago

how many tokens per second do you get?

eklavya• 26 days ago

How are you running these together, splitting the model somehow or did you mean different models on any one card at a time?

brcmthrowaway2• 26 days ago

Dual 3090 >>> Any Apple product.

gnabgib• 26 days ago

Dual 3090 >>> Any Apple product.

Dual 3090s are terrible airpods

tim-tday• 26 days ago

I snorted

kylec• 26 days ago

Doesn't the 3090 cap out at 24GB VRAM? That's not a lot to run a local model

mzubairtahir4• 26 days ago

but still it can run handsome models

brcmthrowaway2• 26 days ago

https://old.reddit.com/r/LocalLLM/comments/1ug428c/4x3090_19...

nichch• 26 days ago

My opinion is that you should wait for 6-12 months before making a purchase either way.

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.

Frannky2• 26 days ago

This is my opinion too. Even if you buy hardware like a cluster of 8xGB10s or 4 A100s, they'll still be slow and a little dumber than what you're used to. We need to wait a little for better hardware. Lots of companies are pushing the frontier, so hopefully it'll come very soon.

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).

gizajob6• 26 days ago

The hardware is here today for people prepared to tolerate mild amounts of latency. It’s easy to forget that computing tasks used to often take major amounts of time - rendering an audio file, rendering a video, transcoding – all kinds of tasks took minutes or even hours of the computer spinning its fans on maximum just to deliver the result. AI and agentic AI and diffusion is the next round of that - trading a small bit of your waiting time for phenomenal power. The datacentre builders trying to get you hooked on instant responses on the LLM platforms have made you think that a “good” AI responds instantly and completely interactively - they can still be brilliant with a bit of delay. And having a competent agent doing things on my local machine, it doesn’t really matter if it takes ten minutes or an hour or six hours to complete a task while I’m out doing other things.

FireBeyond2• 26 days ago

The racks we're deploying are effectively GB300 NVL72s: 72 Blackwell Ultra GPUs 36 Grace CPUs, 20.7TB of unified HBM3e.

Works out to about 1.1exaflops of fp4. Networking is 800gbps.

120kW per rack.

speedgoose3• 26 days ago

The OLED touchscreen MacBook is rumoured to be called MacBook Ultra now, and it has been delayed quite a few times. I will probably cost the same than a decent bike.

gizajob6• 26 days ago

MacBook Ultra Expensive (when equipped with 256 or 512 Gb of ram)

cassianoleal3• 26 days ago

a decent bike

I bought a decent bike a few months ago for a few hundred bucks. I used it for commuting and for when I went to the park.

Of course, it depends on what kind of bike you meant and what you consider decent. Hopefully that illustrates the quality of that comparison.

cylentwolf• 26 days ago

I asked a few of my friends that are ML engineers this question and all of them said to run the LLMs in the cloud with their infrastructure because it was going to be way faster. If you just want to tinker around I would look at @JSR_FDD's comment.

derwiki2• 26 days ago

There are more factors than speed though, like: privacy

throwawaytea3• 26 days ago

Next you're going to tell me that car salesman recommend just leasing new cars, doctors recommend just following the standard of care, construction workers just recommend subbing it out, and your tech friends say just use AWS.

ramraj07• 26 days ago

If your car salesman friend gives you stupid advice it either means he is stupid or he is not your friend.

al_borland• 25 days ago

My dad avoided buying a home PC for a long time, because he felt the systems he used at work in the 80s were so much more powerful than anything for the home at the time, that he didn’t see a point.

I can’t help but draw parallels here.

SenHeng• 25 days ago

And he wouldn't be wrong for that era. Prices also dropped pretty quickly while specs would almost double within a span of months. Whatever he could've bought in Jan would be easily outclassed by anything 12 months later.

We don't have that anymore. Specs have more or less stabilised and what you're buying now could easily last you years.

gizajob6• 26 days ago

Local LLMs running in LM Studio on a MacBook Pro work great, if you’re prepared to wait for the answers because using an LLM locally is much much slower than having the instant results appear when using an online LLM like ChatGPT or Claude. You can also run OpenClaw on the MacBook and have that act as the front end for the LLM, to get full interactivity and have it install command line tools on your Mac to perform whatever tasks you’ve set it.

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.

browningstreet• 26 days ago

It’s kind of amazing how steadily this question is asked in every forum where it can be asked. Kind of amazing that the answers previously given can’t reach the next person who’s going to ask it.

derwiki2• 26 days ago

Isn’t it all rapidly evolving? I guess beyond that it’s a signal of growing interest in local LLMs

throwawaytea3• 26 days ago

What's crazy is these people want to invest decent money to use AI/LLMs, but somehow don't think to just ask an AI chat bot which would easily answer it for them and any follow-up questions.

g-technology• 26 days ago

Around February or march I started looking into hardware options to help me start learning about training models and working with them. My budget was limited and an apple refurbished 32 gb Mac mini was far and away the best option for my budget. I wish it was faster but I can let it run 24/7 with no noise and minimal power draw. I just arrange long running tasks for when am asleep or at work. Then as a huge plus I have an awesome daily driver machine for whatever else I want to do

alecco• 26 days ago

MacBooks have lots of RAM and no PCIe bottleneck, but ~10x fewer FLOP/s than a much cheaper Nvidia GPU. Test LLMs on rented GPUs on vast.ai or other similar services (beware storage etc). Don't spend thousands before trying and knowing exactly what you get.

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.

EagnaIonat4• 26 days ago

With a dedicated GPU, the lag is in transferring data to the GPU. You don't have that lag in ARM.

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.

dust42• 26 days ago

With a M5 16c 48GB and Qwen 3.6 35B Q4 I get up to 1900 PP/s and 80 TG/s. With an Nvidia 5090 I get 7800 PP/s and 280 TG/s.

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.

tosh• 26 days ago

macbooks (macs in general) are a good package for llms because they come with so much RAM

and for llms more RAM means access to better models

macbooks might not be as fast as a GPU with similar amount of RAM but more affordable and well integrated

last but not least: compared to a PC+GPU the macbook is either silent (air) or at least way less annoying when you care about noise

for ultimate flexibility and low noise: GPU in the cloud for when you need/want it is probably also most cost effective if you don't have workloads that need to run 24/7

sfifs• 26 days ago

So a lot depends on your specific use case but mid-sized open weight models are pretty actually good now, so this is realistic [1]

The first question to ask is does your use case require handling personal or sensitive data.

If you're using the LLM for OpenClaw or you want to handle sensitive or medical data, a local model generally is necessary.

if it's not so sensitive - Cloud providers with some sort of user agreement guarantee on not using your data for training would be the next bet. I personally generally use Gemini or Sonnet as my cloud backup. As I understand, OpenAI, Cloudflare (which bought replicate) and Qwen also seem to provide such guarantees and make SOTA models available. Others like DeepSeek seem to have an opt-out setting. Open router & co I avoid except for benchmarking models with public or dummy data as there is absolutely zero guarantee or ability to enforce terms on providers where your data might be sent.

Gemini and Anthropic (and OpenAI) tend to be expensive - it's very easy to run up 15 dollars a day or so bills which puts you solidly in 1 year pay out on Mac Mini territory - at this point I decided to buy. Gemini Flash Lite 3.1 is however surprisingly good value.

the next question is Mac or CUDA. If your expected use is serving LLM models for inferences, the latest large memory Macs give pretty good inference speed (better than DGX Spark) at a reasonable cost - I think there offer much better value than CUDA if the only use case is LLM inference & harnesses.

if you plan to also fine tune models, experiment with other types of ML on GPUs, do computer vision stuff etc. the development tooling on CUDA is far in advance of all other platforms.

Lastly if you choose CUDA, the question is GB10 family (DGX Spark - cluster able with 128Gb RAM et all) or dedicated GPUs workstations. What I found is practically any serious models weighs in requiring at least 96GB VRAM - Antirez's 2 bit quant of Deepseek 4 flash (my current daily driver) [2] , the Qwen 3.5 122B A10B 4-bit quant, the Qwen 3.6 27B Dense and 35B A3B 8 but quants etc. So you're well out of the consumer GPU territory into 1 or more RTX 6000 Pros or Data center grade devices. Yes you can try to hack away with multiple consumer cards or SSD streaming but it's very fiddly and you probably have better things to do with your life.

The GB10 system - which I ultimately went with - is certainly much cheaper and can be clustered through the Special NVLink cable to get 256, 384 or 512 GB setups but comes with severely constrained bandwidth. The Pro GPUs blast these out of water on performance but are expensive.

Lastly, renting a cloud GPU machine doesn't really make sense except to run already debugged fine tuning workloads. You'll probably spend at least 4 dollar an hour for sufficient capacity which if it's personal use, will mostly sit idle.

1. https://srinathh.medium.com/mid-size-local-models-are-now-co...

2. https://github.com/antirez/ds4

zihotki2• 25 days ago

From personal experience - it works, but you won't get a comfortable time to first token (latency is high). The reason is that prefill on Macs is bad. You need to have a lot more cores to do it quick. It's close to instant for small models on NVidia GPU's but on Macs it takes a few seconds to get the answer for a simple prompt. And the time grows proportionally with your context size.

hkchad• 25 days ago

It depends. I have a M5 128 so i can play around with large models and even keep several of them loaded at once and use something like llama swap to access them all via bifrost or litellm. You won't do this without some serious local GPU's with big memory. The downside is the speed, it's not fast, but fast enough to tinker and develop with worrying about ongoing cloud cost. When done developing and you need to really scale up this is when you can swap to cloud computing and get the job done faster. My $5k macbook can do more than a $50k nvidia/intel/amd setup, just not as fast.

So you need to decide whats important to you if you want to work locally, large/many models or speed. It's the pick 2 problem speed, size, cost, pick 2 or go with cloud and accept your development time is also spent $$ on each iteration.

I was hoping for the M5 ultra by now, but looks like that's not coming until much later this year for a much higher price now.

lifesucks1• 24 days ago

I wouldn't buy any Mac products for LLM usage. It's cheaper to just buy a subscription. You enjoy Mac for your personal/professional use. I have been running LLM on Mac, honestly for such an expensive machine I wouldn't want to use it for long runs just for a quick 30 minute usage. It heats up so much you can make an omelette on it. I would NOT want my dedicated MacBook run hot for long these things last forever. Fairly confident if I run LLM's all day long their life will be short lived. In my family my kids are still using their laptops which are papas handovers. I have an iMac from 2014 that my kids still use.

octopusx• 23 days ago

This multi-model unified gateway addresses a huge pain point of juggling separate billing dashboards and rate limits across dozens of LLMs. The complete LICENSE and contribution docs make extension very approachable for external developers

4eric• 22 days ago

I think nothing beats graphics cards—you can always add one or upgrade to a newer model.

yahavthehackern• 22 days ago

How bad is the token-per-second drop-off on the Mac once you scale up the context window? Does it hit a wall at 32K or 64k tokens?

Ask HN: MacBook vs. Dedicated GPU for LLM

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