5 years of (200/month) tokens at that price, meanwhile an rtx 5090 pc is about half that… hmm
but i wonder how much these token costs are sustainable or not, it may be in the long term cheaper to have your own hardware if token costs go up (and hopefully hardware gets cheaper again)
The token price isn't the only reason to run a model locally though. You can do additional training to specialize or remove censorship that may be a no-no per TOS with cloud GPUs.
Those tokens aren’t guaranteed (esp. with RE and security tasks - rooted my own TV last week, Claude crapped out on “cyber safety” grounds; but also no guarantees about the model served - providers can pull a switcheroo on weights or quantization at any moment, and new options may not work for you), and you’re throwing money at entities that aren’t aligned with your interests instead of entities who are interested in actually empowering you.
A $10/mo subscription to OpenCode Go would have done the job for you.
They have models like Kimi K3, Grok 4.6 , GLM-5.3, Mimo 2.6 Pro (launched today, already available) which are happy to follow your orders without accusing you of being a terrorist.
They do MoE. They benchmarked GLM 5.3-flash (320B / 18B), and Qwen 3.8-flash-next (125B / 6B). The dense Qwen is only focused (I assume) because it's about the only thing that fits on a 5090, that they can compare the two heads on.
A dense model (up to the amount of memory available) actually does make the most sense on unified memory architectures. But when you hit the limit of what you can hold in memory, you reach the limitation of the platform.
Whereas a hybrid architecture with distinct DRAM and VRAM with sparse MoE, you can leverage two different bit rates depending on the actual need for constant access to common layers versus sparse access to infrequent layers and arbitrage the difference in cost for each of those in distinct classes of hardware.
This 1000%. Data centres don't equate to medium sized labs and businesses. A stack of Macs is up and running without digging trenches, an electrician on staff and a department of PhDs to justify the spend.
It's likely that a stack of Macs will draw more power for slower prefill/decode than equivalently priced Nvidia GPUs. If power efficient inference is the goal, Macs are a non-starter.
brother my point is they don't care about their product offerings outside of their phones. this post/thread is about one of their product offerings which is not a phone which is inferior to their competitors'. simple.
Exactly; when I first got my RTX 5070 Ti (16gb, to game with!!!, upgrading from VEGA56), I loaded then-latest Qwen3.6 (~30B, cannot remember exactly). My only prior LLM experience was with models <8gb, primarily llama3.1.
My technical-expert twin played around with these LLMs, for about an hour, and then correctly reasoned "it's able to be WRONG, faster."
This seems apt. My next LLM machine will be closer to 96gb+ vRAM.
A) the macos value add is enormous if you have any investment in the ecosystem, B) for me at least a GPU is completely useless for anything but being a token generator.
No thanks to the "macos value add" that forces you to use Metal while Valve customers frolick in Protonland.
Crossover works on macos, too. So does moltenvk, so does vanilla wine, etc etc. You can run most games without a hitch these days (allegedly, according to /r/macgaming). But I don't play video games so a GPU would probably be better off in some kid's computer.
A GPU would be better-off attached to your Mac in an eGPU enclosure. There is not a single Apple Silicon GPU on the market that leads the industry in prefill, decode or power efficiency.
But of course, Apple doesn't allow that as part of their ecosystem. It's really a privilege to have MoltenVK perform worse than the fanmade HoneyKrisp driver. It's valuable when Apple refuses to sign AArch64 CUDA drivers for macOS. It's exciting to pay Crossover to support half of the library Proton offers for free.
Clearly, I'm some sort of ingrate that selfishly demands the best things, without considering how to accommodate the poor trillion-dollar megacorporation.
A 5090 has a 1.79TB/s memory bandwidth. Qwen 3.8 27B NVFP4 is 22GB. You cannot generate tokens faster than the weights can traverse the GPU memory, so that makes max generation speed without MTP to be 81T/s. Say MTP is giving you 0.5 acceptance rate (very good), that is 1.5 * 81 is 121T/s. Even with a perfect acceptance rate you would only get 162T/s.
Off the top of my head, I'm guessing we're missing sparse attention. But I'll run your challenge through and see where the gaps are. I promise I'm telling the truth :)
Have a 5090, and yes it's very fast. But it's like the worst ADHD team member and requires constant supervision and review from larger models. It's context size on-card is good for super, suuuuuper shallow precision work.
The gb10/spark on top of it, that thing can refactor enormous monorepo architecture. The time it takes the 5090 to compact, reiterate and execute a plan is often the same time as the gb10.
Personally I think the price is way too high right now. It’s a power hungry gaming GPU. The efficient single card equivalent would be a 4500 Blackwell which launched at about $3500. Or you could get a 9700 32GB or an Arc B70 for well under $2k, today. You only buy a 5090 if you want absolute speed.
32GB is still not that much. I would rather get a Spark and have the RAM to experiment with larger LLMs, even if it was slow.
Thank you for this. I wish Apple focused their silicon design on improving the TTFT metrics but coming from an M3 Pro, it still looks laggard compared to Nvidia's TensorCores in the 5090.
Maybe Apple is an acquisition away from changing that balance.
The rumor on Apple's processor roadmap is that they're skipping other M6 variations (all previous generations had Pro and Max, a few had Ultra) in order to focus on the M7 generation for AI reasons. What exactly the M7 improvements are who knows.
I think that comes down to TSMC. Nvidia apparently booked out the whole A18 or 16 node. Apple is on 2nm right now and M7 will jump right to A14. According to my quick AI research anyway.
Yeah, Apple has spent 3 years each on the 5nm and 3nm nodes with TSMC. There are some reports [1] that it will jump to 1.4nm after 2 years due to AI but there's no real proof. The source is Digitimes who are frequently wrong with their predictions and rumors.
Apple's planned M7 Ultra chip is being designed to support up to 1.5 TB of unified memory and to push AI performance toward the class of Nvidia's Blackwell accelerators
The selling point of the M5 Ultra Mac Studio is that you can run much larger models that the 5090 can't without swapping. NVidia aggressively segments the market on VRAM for this reason. That's why a 5090 has an MSRP of ~$2k (but good luck getting one for less than $4k) while a 6000 Pro, which is basically a 5090 with 96GB of RAM has now soared beyond $15k where 3-6 months ago it was more like $10-11k. A 6000 Pro has the same memory bandwidth but slightly more CUDA units (IIRC ~24k vs ~21k).
This advantage won't be apparent with a 27B model. The 256GB MS can probably run the newer Flash models locally, something you can't do on a 5090.
I don't think we'll get a successor to the 5090 until late 2028, maybe even 2029. I'm basing this on the launch date of the 5000 series and that we haven't got a midcycle refresh yet. Rumor has it the chips are ready but the 3GB RAM modules are 3-4x the price of the 2GB modules used on the current cards.
Apple should see a Mac Studio major update in 2028. That might even force NVidia's hand. But it's really impossible to say what the state of the market will be 2-3 years from now. It may have completely crashed. I suspect not however.
The interesting thing will be when the bandwidth demands start forcing HBM memory onto these home/enthusiast solutions.
But what about builds that combine 8 of the 5090 with infiniband between boxes? Wouldn't that be comparable to the mac in terms of price and potentially beat it by a lot in terms of performance for the large MoE? I understand the space/heat/noise considerations, but price wise it may still not make as much sense as people think. (Agreed that it is hard to get the NVIDIA hardware and the 6000 pro are priced less competitively).
While that sounds super awesome,
How many people are actually going to build and maintain that vs a box you can grab at the mall that fits in a lunchbox?
I can't speak to Infiniband pricing for something like that. It seems like the cheap option is 56/100Gbps with used Enterprise equipment. You'd need 8 HCAs, DAC cabling and a switch but even then you're into thousands of dollars. If you want 200Gbps+ it gets into the tens of thousands (AFAICT).
Each PC is probably going to cost ~$6k and you're talking about 8000W of electricity draw. That's going to consume multiple 20A circuits even at 240V. And the electricity ain't free either. A Mac Studio seems to draw ~500W max.
Oh and the Mac Studio has an upgrade route to run 1T+ models too by chaining them together with TB5 chaining. OSX supports RDMA this way. That's comparable bandwidth to the 100Gbps Infiniband option.
So you're talking about $50-60k of hardware and more power draw and more heat for something that will I'm sure beat the MS M5U option but at huge cost. Also, at that kind of price point, I'm likely to get a workstation PC and put 2 (or possibly 3) 6000 Pros in it.
Others have chimed in on cost and size, I'll chime in on power. The 8x 5090s will require a dedicated datacentre grade power source. The Mac Studio runs on a plain jane wall socket.
Where are you buying 8 5090s for under $10k? With CPU, RAM, and (checks comment) infiniband hardware???
You're probably looking at a lot closer to $60k when all is said and done, and that's before you hire an electrician to run a sub panel for your homelab...
Certainly not forgetting wattage. A 5090 is 575W. The M5 Ultra Studio is 480W.
nvidia-smi -pl 450 for like a 4% reduction in throughput. I tend to set it around 350W because it's a comfortable temperature blowing on my legs under the desk without warming my office in the summer.
I put together this system two years ago, so it's a little out of date, but it only cost $3000 for the same performance and capability as an Ultra. I don't think I would spend $7000 to save 100W, though.
nvidia-smi -pl 450 for like a 4% reduction in throughput.
Yeah people don't pay enough attention to those settings IMO. The first thing I do when I set up a new machine (or upgrade my OS) is to restore all my powersaving configs.
For example I've got all but one of my virtual desktops that put the CPU in powersave mode: I don't need max Ghz when browsing the Web, not even on demand. But when I switch to the virtual desktop where my development environment is, then I want power on demand.
Now I don't do it to save the planet: I do it because I love a quieter computing experience (coupled with Be Quiet! PSU and Noctua fans, this makes for a very quiet computer). That it consumes less electricity is a nice side-benefit.
When you are doing matrix math, compute is compute. Apple cant be more efficient due to physics. The only reason Macs are more efficient in general is that they have tightly bundled hw and sw for specific tasks.
Now try running that Qwen 3.8 Next model on the 5090 and tell me what TPS you get (hint: it's near 0 since it doesnt fit the 32GB VRAM on 5090 vs the 256 in OPs M5).
Surprisingly, the Reddit crowd are reporting 50–60 tokens/s (for the 32 GiB 5090 + 128 GiB RAM)—on par with the M5 Ultra benchmarks, despite both the PCIe bottleneck and much smaller DDR5 bandwidth,
Apple product won't be the cheapest but it is a full package (CPU, RAM, VRAM/GPU, fast-storage, etc).
If you look at the pricing of a full (x86) AI workstation you'd need around the nvidia GPU, you'd approach $10k easily (and be using a ton more wattage too).
I have a 3 year old gaming system. RTX 4080 w/128GB of DDR5. It runs Qwen 38 Flash around 44-40 t/s with 128K context. It is on a specialized build that caches MoE experts and uses an optimized 3bit quant that basically is within a few points of the full 8 bit quant. In general, in casual benchmarking with Alibaba's endpoint I could not tell much of a difference. Overall this model is very good on long horizon agentic work. The main pain point for it is that its input processing speed is slow. Regardless, it gets meaningful work done.
I assume those are non-batched. I think the M series GPU can do 4X to 8X depending on model quant, which means if you can batch queries you'll get almost 4X to 8X performance.
On my m5 max 27b model does 75tps on 256k ctx and starts at 80 on the 8k ctx when you add https://huggingface.co/collections/z-lab/dflash-2 to it. So yeah base might be 30tps (I used iq4) but mtp or dflash help a lot and should be used when checking what is useful and what is not for running models as it is not fare to judge without them.
Apple's planned M7 Ultra chip is being designed to support up to 1.5 TB of unified memory and to push AI performance toward the class of Nvidia's Blackwell accelerators, according to a new Bloomberg report published by Mark Gurman...
Apple plans to release a base M6 chip this fall for entry-level Macs... a base M7 in the first half of 2027, M7 Pro and M7 Max at the end of 2027, and the M7 Ultra in 2028.
I really appreciate seeing these dense model numbers. For a large unified memory system though I expect that MoE numbers are what people are more interested in.
These numbers could and should get much better. As an example I can run Qwen3.8-27B-MXFP4 (W4A8) on 2x AMD R9700 that gets 260+ tokens/sec to start and slows down to ~110 tokens/sec over 128k context and can do the max 256k. These are for batch size 1 and throughput goes higher with batching. This is due to speculative decoding, efficient all-reduce inter-gpu compression, and custom GEMM kernels for the specific hardware. Note each R9700 only has 644 GB/s memory bandwidth.
A basic llama-bench on Qwen 3.8 27B UD-Q4_K_M gives pp512 3920 tok/s / tg128 81 tok/s on a 500W RTX PRO 6000 (should be similar speeds to a 5090, chip is basically the same, just less VRAM). With MTP3 this is 140 tok/s on mtp-bench.
This is with llama.cpp. You can of course use vLLM/SGLang well on these cards and they're even faster. On vLLM w/ NVIDIA/Qwen3.8-27B-NVFP4 baseline has a prefill of about 13,000 tok/s. The baseline tok/s is 72 tok/s, but at mtp7, it's 157 tok/s, and w/ dflash7 that goes up to 215 tok/s. On mtp-bench, DFlash2 gets a hair under 300 tok/s w/ the code_python prompt.
On Apple website it says 512GB memory option is available in October. I guess bumping to that one would cost additional 4-6k US$. So an Ultra with 2TB storage would be north of 15k US$.
That’s like 12 years worth of OpenAI Pro subscriptions
I think we all expect the heavy subsidized subscriptions to end or significantly increase in price at some point, but it could be years from now and I'd rather spend a similar figure on an hypotetical Mac Studio M8 Ultra, or whatever more advanced competitor that will have likley appeared by that time.
A more apples-to-apples comparison would be with API cost in OpenRouter at the same tok/s rate for the same models that you can run locally, maybe.
Is there evidence that's true though? I mean gross margins on subscriptions being negative since the API is seemingly very profitable (if the price is compared with the cost of serving very large open models).
As long as there is pressure from other providers serving cheaper models that are somewhat competitive without having to incur any of the R&D costs raising prices will be tricky.
HN always has these completely contrived counterarguments. What is actually going to realistically happen that will prevent use of an LLM provider? Did you think that the OP literally meant the 12 years or maybe it was just to show how expensive using a Mac Mini as an alternative is?
Yes, it feels like that. Whereas frontier labs are pushing the frontier of human knowledge, selflessly working towards pulling humanity from dark ages. Ignorant peasants trying to burn the modern civilization down. Don't they know data centers and fiber lines are lifeline of modern economy?
how expensive using a Mac Mini as an alternative is?
I think it goes without saying. And it is eminently evident over last couple of decades that from compute to storage to meals 3rd part providers have saved billions upon billions of dollars to enterprises and individuals alike by providing these essential services.
Yeah, anyone who thinks local AI is going to save them money is likely to be disappointed, at least if they want to run models that are even remotely capable.
Plenty of other reasons to get excited about local AI, but I don't think cost is one of them.
Maybe you are using a local model to go after some Millennium Prize problem and you don't want OpenAI to take your work and use it to win the prize for themselves? $15k might be a bargain.
And, yes, I know a current local model wasn't going to solve the Navier-Stokes problem, but I'm just using it as an example where privacy might be valuable.
Despite being on a site called Hacker News, we seem to often overlook the simple aspect of wanting local AI hardware to hack (not necessarily in the cybersecurity sense) with. I got my local AI hardware because it's an enjoyable hobby for me.
apparently if you ever point out HN starts for hackernews and thus expect related attitudes you get downvoted by shocked ( what I guess are zoomers and not bots ) that desperately opine the name is a random abberation doesn't mean anything and one should not deviate from our corporate overlods in any manner.
A lot of people commenting on how bad idea buying local hardware for inference is also miss the fact that even in 3 years that hardware gonna cost something.
Might be if RAM prices get much more reasonable its gonna be 1/3 of the price, but it's very much possible its gonna be half or more.
And if you're buing Mac Studio and not some AI-only locked down board it's possible to reuse it for other purposes.
Hard to guess, it can go either way. If you will need to be in a syndicate to use non-sterilized models, that mac makes sense. But if there is mandatory registration of personal cyberarms, you risk going to mines once they check you purchases. You could try to play normie and pretend you simply wanted to show off, by keeping your actual work on external disk, but that leaves traces on system. Counting on someone in the Gap renting you gray iron works as long as you can swap credits. Still, this gear is tiny. Put it in your e-car, with uplink, and leave it at uncle's farm. Discreet.
It was a dark rainy night in Neo-Tokyo as Blake puffed on his vapor cartridge and watched the Mac dealers prowl below. Almost 15k Union Credits to get one of them to meet you in an e-cafe with a fully loaded M5, but man, the inference rush from one of those things was something else.
Just commenting here because you're discussing hardware: I thought the test results from the SSD published in this article [1] were pretty interesting. Maybe that's old news though.
512 option isnt worth it imo, you get severe slowdowns when weights are that large. 256 is the sweet spot, you can run large open weight models at decent speeds for full private inference.
a) we don't actually know what the prices will look like yet,
b) what about same weights + huge context? or, same weights that you'd run on 128gb/256gb, but multiple models running for different tasks?
I think it's safe to start the conversation as about bad as the jump from 256 to 512 on the M3, which was a little more than double base to 256. If it's surprisingly different at launch then it can be a party, but there is no sense getting your hopes up for that at the moment.
Longer context also slows token prediction proportional to the context size. If it wasn't regularly referenced then there would be no need to keep it in RAM.
Usually the pitch for more memory is "I can run a massive model/context and get my answer in a while instead of next weekend from disk".
You definitely need more RAM if you are not satisfied with small context windows, especially if the weights take a large % of the total memory to boot.
You might be. Running another agent doesn't load a set of new weights. It creates a new KV cache for the agent and adds the prompts to the queue. Its just another inference turn.
"It also happens to be a Mac, with an operating system that looks nice and doesn’t suck"
Yes Apple has some of the best hardware out there, albeit overpriced. But the software is such a hindrance and I can't take anyone that states otherwise seriously. If only it had proper Linux support (and the Asahi people do an amazing job but you can reverse-engineer only so many stuff with limited funding, and then you have to do it again for new models). MacOS is good if you just want to have a standard experience, which to be fair is most people. It's good for just setting up an LLM server I guess since the hardware is a perfect fit. I wouldn't touch it otherwise.
Comparing to Linux is too general. You need to say what distro and what install level. I really only use headless Linux, but I've never logged into a fresh install and not had to do some sort of 'apt install devel-packages' equivalent for which ever distro being used. That's the same thing with macOS after choosing which package manager to use. I don't see how Linux vs macOS is very different
This is great as a first look, but the author is not a developer, so we don't yet know whether a dev can be as productive with local models on M5 Mac Studio compared to a 20x subscription plan.
I'm also curious about any new low hanging optimization opportunities in the kernels for this new hardware.
It's already clear to me that M5 Mac Studio is more cost-effective than anything you can run on open router, assuming decent utilization.
The M5 Mac Studio will be the most cost effective way to run uncensored cyber capable open agents.
An exciting tipping point will be if programmers can get an Astra-Ultra like experience all week with this hardware. That would be a real sense where this hardware exceeds the value of even 20x cloud subscriptions.
This is great as a first look, but the author is not a developer, so we don't yet know whether a dev can be as productive with local models on M5 Mac Studio compared to a 20x subscription plan.
Local models are definitely not as productive as SOTA, sadly it's not close yet. I do think someday they will be "good enough" to use, but they aren't today. Even the SOTA models barely code well, with Opus 4.5 being the first, good coding model.
That being said, I think it's absolutely imperative that we keep pushing local model performance. We need to continue to advance technology there and ensure that the model labs don't do regulatory capture in the name of "safety" (or anything else).
With the latest codex (weekly quota burn) fiasco I tried open weight alternatives for the first time. And tyeah... open weight models cant compete with likes of astra yet. But, my hope is that by the time I get my Mac studio at end of november an open weight models would have closed the gap (which i think is realistic at the speed of progress). Now its true a better gpt version will also be available then but it also seems the gap is shrinking with time so theres that.
And tyeah... open weight models cant compete with likes of astra yet
I think this is true, but also misses that a lot of us are just doing basic flask apps with a react front end. We don't need astra; Something sonnet 4.6 level locally is perfectly sufficient 95% of the time, and maybe 99% of the time.
This. People have convinced themselves that the absolute frontier is what is needed, anything below it is an unacceptable compromise, and we seem to be speaking different languages when it comes to discussing model capability.
It's like watching a discussion about cars available to take on a 100km road trip. A new car gets released that is on par with a Toyota Corolla but it is dismissed as completely useless for a 100km trip because it doesn't have the seat massagers and air ride suspension that the new Escalades have.
The reality is that something like Sonnet 4.6 is still amazingly capable for so many programming tasks, especially if you already have some reasonable level of experience to steer it in the right direction.
And if you think Sonnet 4.6 is still worthwhile, then it seems undeniable that something like Qwen 3.8-27B is also worthwhile.
Local models can be widely used as productive assets. Yes the infrastructure of SOTA API models is engineered specifically for you to be that utility, but the blanket statement that local isn't up to par is intensely short sighted. Billions of tokens per month on local pays for the hardware when compared to sota costs per month.
I believe they can currently be used productively for non-coding tasks (classification, light summary)... but they definitely are not even close to SOTA when it comes to software development.
Defining productivity is a use-case scenario, and a wildly generalized assumption for most people in this argument. Local infrastructure doesn't need to be sota for absolutely every single need for a dev lab, but it absolutely can be delivered with non-api frontier class models.
Just to be clear, I'm specifically talking about coding. I think local models can help with productivity today, just not coding.
I'm also a huge fan of local models and think it's absolutely imperative that they continue to advance so we can move off of the Anthropic/OpenAI hosted models. It's important to accurately asses where we are in that journey though.
I think the issue is generalization, if you were more specific about which local models aren’t good enough for which tasks compared to which frontier models in your experience, it’d be a lot more informative
Like the other commenter, I'm confused about the 'just not coding' conclusion. I'm using Qwen 27B on a 5090 at > 100tk/s with 150k context (which isn't enough admittedly), and DeepSeek v4 Flash with 1million context on a gb10/spark. Both of which are performing surface level, and deep needle precision infrastructure architecture. They code 24-7, stupendously.
It would be interesting to hear more about how you’re actually using them. Do you have sophisticated feedback loops around the models so they can verify their work and converge on good solutions? And how do you decide what to give the 5090 vs the Spark vs a frontier model?
Correctness matters much more than speed to me, but if I can get both, that’s obviously very interesting.
Astra-Ultra? Even the largest open model to date (Kimi K3) is nowhere close to Astra level, and it will be quite slow even on the highest-spec M5 Ultra, with achievable speeds of about 0.5 tok/s at most due to having to stream weights from SSD (~13 GB/s on the highest storage capacity M5 Max machines so far). This is OK for doing simple Q&A in the background but it's far from a genuine coding experience. You'd have to test batching of multiple thinking streams in order to try and raise overall tok/s via layer-wise reuse of the streamed weights (and this is where the "Ultra" part sort of becomes relevant; Kimi series models have good support for agent swarms) but this would decrease single-session performance even further. It would only be usable for background jobs, though the hardware would then have a chance of paying for itself if it was fully used on a 24/7 basis.
That's all well and good but Qwen 27B is a small, dense model; that's favorable to both batching and MTP. Batching of large, sparse/MoE models like Kimi K3 (requiring slow SSD streaming even on a single maxed out Mac Studio) on local hardware is an entirely different game that's mostly theoretical so far: many people would even call it outright pointless. (MTP clearly fares even worse, though - unlike batching, it ends up wasting scarce weights-fetching throughput on wrongly predicted tokens.)
Unless you only need the AI available some of the time, $5/hr is pretty expensive. That's an RTX Pro twice a year.
If you're using it for discrete sessions of coding or something, that might make sense for you but if you're using it for an always-on assistant, that pricing kinda sucks.
While I know it's not apples to apples, the target comparison right now is 2x DGX Sparks. Similar price, 256gb. The conversation has focused on memory bandwidth vs. compute in agentic loops, so for most people the raw numbers will mean less than the "time per task" in coding benchmarks.
This is a great article and bodes well for the M5, but we should expect more like this comparing to other platforms before we truly understand where it fits.
Speed vs task-completion is a new conversation and a great point. Whereas the cost to compute doesn't exist in a vacuum, making mistakes costs less, is easier to maintain with granularity and a whole host of other factors when you own the lab.
It's not the M5 Ultra itself, but the M7s or M9s that will do the damage.
99% of people will use whatever AI is free. The sophisticated, heavy users that are willing and able to pay a lot of money the ones that will be interested in controlling their inference bills.
Today, the sweet spot where an M5 Ultra makes sense is tiny. But we might expect that to grow a lot.
Even if you could get a frontier model, you would not be able to run it on any Mac. So speculating on what M7 or M9 will achieve in 5 years (if we even still exist) seems pointless.
True, but do you need a 10,000 dollar machine to do so?
I would love to find an excuse to buy a 10,000 dollar machine! But I cant find one yet. My current cloud bill is in excess of 400 USD per month. Just can't achieve frontier model capabilities locally.
You will. Some of us are going to be already ground into dust that the microwave parts are made out of. Others will be locked into our communism cubes with our daily allotment of entertainment and sustinece. let out into the sunlight for only 30 minutes per day.
And nightmare fuel is just what they'll be selling at the UN this week, for this very reason.
Sam's address will probably be more riveting, imaginative, and terrifying than the last couple of Terminator screenplays. Legislators will lobby him to write the laws for them, and the ghost of Harlan Ellison will threaten to sue him.
I don't see how that math works? This is a $15k rig under benchmark and per the results it competes very acceptably against... one consumer GPU.
I really don't see who buys this, except people who want the Studio for some other reason. But nothing in the story says you want to fill racks with these instead of Blackwell or TPU parts; it's not even close.
Your math is correct, but it’s math based on today’s economics.
Think of a company like Apple moving onto your turf. They’re not going to cede AI to the cloud. They want their part of the pie.
So in 7 years, how much AI will be handled locally on your iPhone. And will you have repaid all the debt on your balance sheet before Apple eats your lunch
I think most important thing is that Nvidia doesnt want to give 100% of market to frontier AI labs either.
It's way too easy for 1T+ frontier labs to ditch Nvidia. So Nvidia will also put effort to make sure there are open weights models and local hardware available.
There is zero chance that an LLM approximating a modern frontier model is going to be running on a phone in the next decade. Even if you grant that you could stack enough DRAM dies on top of each other in the package, that would be a three order of magnitude improvement in power efficiency just for the compute.
The irony here is, thats hobby hardware. You spend 20k and can run slow hobby models that are barely capable of anything unsupervised.
Entry level serious hardware starts at 100k, and a bit better but still almost-useful grade is 200k (8x rtx pro, plus a nice epyc pairing). Thats the sort of thing a salaried expert lets their employer buy them for sort of serious work.
Anything really serious is well north of 1M - not including the housing and commercial grade mains connection. And at best that buys fast Kimi K3 or GLM.
Ordered one for OpenFOAM. Excited for it. Will be nice to not have my laptop running CFD 24 hours a day, but my M1 Max is currently my fastest machine… I’m expecting about 3.5x from the M5 Ultra.
I think Apple is really sleeping on making this run a Linux server. These things are very capable and draw very little wattage when idle. It would make an excellent homelab device, but MacOS currently holds it back in this regard.
I'll probably manage with Mac OS well enough, but my linux distro comes out of the box with all the latest OSS tooling I'm familiar with, plus a package manager, and it has linux cgroups and namespaces that power the container technologies we all know and love.
If I switch to Mac OS, I have to sort out a package manager and install all the stuff that's missing, and when it comes to containers... they're just linux VMs. I'd happily cut out the weird proprietary middleman if I could.
My mac is 5 years old. I don't think I can comfortably buy a new one right now. It has a 16GB unified RAM. Honestly that would be enough for so many local models that I want to use but can't use. Because RAM usage (even with literally every single user installed app quit/stopped) the RAM usage is very high that I can barely safely get 6-7 GB (I am supposed to get ~10 GB, but it goes up and down real fast!). That's a shame. If only I could install an alternative OS that uses very little amount of RAM :-)
When people benchmark MLX related quant models, they really need to publish numbers on benchmarks. You cannot take this as it is what you get of the original models. MLX uses pretty simple quantization methods so at lower bits without QAT, it is just not as good quality as llama.cpp ones.
At this point I think I will get the DGX gb300 workstation though I will wait a bit more for the cold season. It is double the price but at least is the real thing
I think we'll eventually get to the point where folks will have a local AI agent but I think people need to temper their expectations to a degree. You aren't going to have data center level tok/s from a box sitting under your desk and you don't need instantaneous responses for many workloads. Having a local agent that can execute tasks over a couple days with your supervision that might otherwise take you weeks is perfectly acceptable.
However I also think that Agentic AI is very much not an out-of-the-box solution, local or otherwise, and it takes a high level of technical knowledge to create an effective AI agent. And there's a problem now where most orchestration is fixed on what models are used for what tasks with no ability to weight constraints like cost, speed, and security.
Once you hit the memory you need, generation speed is mainly set by bandwidth, and every Ultra from M1 thru M3 has ~800 GB/s. IMO best ROI for most people is 'cheapest used Ultra with enough RAM'
I setup an eBay alert and picked up a used M2 Ultra that has delivered good ROI (at least, far better than 15k for comparable-for-my-use-case performance)
I think M1 through M3 were compute bottlenecked in prompt processing (hence the very large gap between M3 and M5, in this page's benchmarks, that's not explained by memory bandwidth alone).
Everyone looking at the Qwen3 27B model and the 5090. It’s like saying a Porsche is better than a $5m Komatsu earth mover at moving a 20lb carry-on suitcase. Yes. Yes it is. Why do people spend $5m on a komatsu then, when this one metric shows the Porsche is better? Huh. Show us the “Moving 300 metric tonnes in one load” metric. How’s the Porsche now? Oh, the Porsche is in the Komatsu? Ok I’m getting a bit carried away with that analogy.
I find it funny that the thing always mentioned with this machine is local AI. If you're a local model enthusiast, then maybe that makes sense, but I just don't see the economics working there.
I ordered the same one for work so I could run more local agents at once (many iOS simulators and Xcode build processes).
What do people realistically do with these? It's too slow and du... not SOTA-level for coding. It's way too slow for video. I tried simulating an "Fable herding Qwen subagents" and it takes much longer and delivers a much worse result than Fable/Astra alone.
$12,299
5 years of (200/month) tokens at that price, meanwhile an rtx 5090 pc is about half that… hmm
but i wonder how much these token costs are sustainable or not, it may be in the long term cheaper to have your own hardware if token costs go up (and hopefully hardware gets cheaper again)
The token price isn't the only reason to run a model locally though. You can do additional training to specialize or remove censorship that may be a no-no per TOS with cloud GPUs.
Cloud GPUs have ToS about purposes you’re allowed to crank numbers for?
I thought this only applies to LLM inference providers, but not raw GPU rentals.
$2200 for a 64GB VRAM machine if you are willing to do a bit of work.
* https://imgur.com/mpdorVJ
Those tokens aren’t guaranteed (esp. with RE and security tasks - rooted my own TV last week, Claude crapped out on “cyber safety” grounds; but also no guarantees about the model served - providers can pull a switcheroo on weights or quantization at any moment, and new options may not work for you), and you’re throwing money at entities that aren’t aligned with your interests instead of entities who are interested in actually empowering you.
A $10/mo subscription to OpenCode Go would have done the job for you.
They have models like Kimi K3, Grok 4.6 , GLM-5.3, Mimo 2.6 Pro (launched today, already available) which are happy to follow your orders without accusing you of being a terrorist.
https://models.dev/providers/opencode/
The numbers I was most interested in are tucked away in a chart towards the bottom - the speed comparison of the Mac Studios v.s. a RTX 5090:
A whole bunch more comparison numbers in this section: https://www.macstories.net/stories/m5-ultra-mac-studio-revie...
Those are some incredible graphs, that leap in prompt processing going from M3 to M5.
Also: ~30 token/s on GLM 5.3-flash, locally. (That's roughly Opus 4.8-tier. I think).
/meta Here's a CSS filter that stops those nuisance chart animations,
A dense 27B doesn't really make sense for the Mac. A MoE makes way more sense when you have modest bandwidth but lots of memory.
They do MoE. They benchmarked GLM 5.3-flash (320B / 18B), and Qwen 3.8-flash-next (125B / 6B). The dense Qwen is only focused (I assume) because it's about the only thing that fits on a 5090, that they can compare the two heads on.
A dense model (up to the amount of memory available) actually does make the most sense on unified memory architectures. But when you hit the limit of what you can hold in memory, you reach the limitation of the platform.
Whereas a hybrid architecture with distinct DRAM and VRAM with sparse MoE, you can leverage two different bit rates depending on the actual need for constant access to common layers versus sparse access to infrequent layers and arbitrage the difference in cost for each of those in distinct classes of hardware.
1.2 T/s is not that modest is it? That's very close to an RTX pro 5000
Those RTX 5090 numbers are bad. You can get over 200 tps with ninfer using NVFP4 and MTP.
can confirm.
I dont' know why people spend huge money on these and Spark. The 5090 is running qwen 3.8 at 200+ tps!! That's 1-2 orders of magnitude faster.
same reason they spend huge amounts of money on rolexes when seikos work better (the tech crowd isn't immune from vanity).
If you seriously think apple products are nothing but a status item, you're deluding yourself and probably have been for decades.
This 1000%. Data centres don't equate to medium sized labs and businesses. A stack of Macs is up and running without digging trenches, an electrician on staff and a department of PhDs to justify the spend.
It's likely that a stack of Macs will draw more power for slower prefill/decode than equivalently priced Nvidia GPUs. If power efficient inference is the goal, Macs are a non-starter.
So if it isn't a comparative ability, now it's a power cost issue? This reads like goal post moving.
If you seriously think apple cares about anything other than cell phones, you're deluding yourself and probably have been for decades.
They've been selling phones for less than 20 years at this point? Though I suppose 1.9 is not equal to 1, so it gets the plural.
...did you mean profit? I don't think they're manufacturing iphones just on the hope they delight you. This is also true of Google et al.
I don't get these weird parasocial emotional attachments/beefs people have with brands. Talk to a therapist.
brother my point is they don't care about their product offerings outside of their phones. this post/thread is about one of their product offerings which is not a phone which is inferior to their competitors'. simple.
People don't buy Sparks and M5 Ultras to run a 27B model - you buy it to run an MoE model like Qwen Next which this M5 excelled at.
Exactly; when I first got my RTX 5070 Ti (16gb, to game with!!!, upgrading from VEGA56), I loaded then-latest Qwen3.6 (~30B, cannot remember exactly). My only prior LLM experience was with models <8gb, primarily llama3.1.
My technical-expert twin played around with these LLMs, for about an hour, and then correctly reasoned "it's able to be WRONG, faster."
This seems apt. My next LLM machine will be closer to 96gb+ vRAM.
Once I get some kind of settlement after getting beaten up by a cop my first purchase will be some RTX Pro 6000s.
A) the macos value add is enormous if you have any investment in the ecosystem, B) for me at least a GPU is completely useless for anything but being a token generator.
No thanks to the "macos value add" that forces you to use Metal while Valve customers frolick in Protonland.
Crossover works on macos, too. So does moltenvk, so does vanilla wine, etc etc. You can run most games without a hitch these days (allegedly, according to /r/macgaming). But I don't play video games so a GPU would probably be better off in some kid's computer.
A GPU would be better-off attached to your Mac in an eGPU enclosure. There is not a single Apple Silicon GPU on the market that leads the industry in prefill, decode or power efficiency.
But of course, Apple doesn't allow that as part of their ecosystem. It's really a privilege to have MoltenVK perform worse than the fanmade HoneyKrisp driver. It's valuable when Apple refuses to sign AArch64 CUDA drivers for macOS. It's exciting to pay Crossover to support half of the library Proton offers for free.
Clearly, I'm some sort of ingrate that selfishly demands the best things, without considering how to accommodate the poor trillion-dollar megacorporation.
A 5090 has a 1.79TB/s memory bandwidth. Qwen 3.8 27B NVFP4 is 22GB. You cannot generate tokens faster than the weights can traverse the GPU memory, so that makes max generation speed without MTP to be 81T/s. Say MTP is giving you 0.5 acceptance rate (very good), that is 1.5 * 81 is 121T/s. Even with a perfect acceptance rate you would only get 162T/s.
Off the top of my head, I'm guessing we're missing sparse attention. But I'll run your challenge through and see where the gaps are. I promise I'm telling the truth :)
I think you’re missing that MTP can predict more than 1 token in advance.
Have a 5090, and yes it's very fast. But it's like the worst ADHD team member and requires constant supervision and review from larger models. It's context size on-card is good for super, suuuuuper shallow precision work. The gb10/spark on top of it, that thing can refactor enormous monorepo architecture. The time it takes the 5090 to compact, reiterate and execute a plan is often the same time as the gb10.
Perhaps consider some non-offensive language for your comparison?
Is a 5090 still cost efficent when it is (currently) unobtainable? Or when obtainable only at current prices (min. $6500 USD)?
Personally I think the price is way too high right now. It’s a power hungry gaming GPU. The efficient single card equivalent would be a 4500 Blackwell which launched at about $3500. Or you could get a 9700 32GB or an Arc B70 for well under $2k, today. You only buy a 5090 if you want absolute speed.
32GB is still not that much. I would rather get a Spark and have the RAM to experiment with larger LLMs, even if it was slow.
Qwen3.8-Flash-Next is pretty damn worth the extra ram you need.
How are you deciding which work to send to the 5090 vs a frontier model, or making the two work together nicely?
Correct is much more important than fast for me, but if I could get correct and fast, that would obviously be amazing.
Both are probably single-token decode performance, which is reasonable to show. Otherwise agree RTX 5090 should shinebetter with NVFP4.
... or with llama.cpp with MTP.
The issue is that the moment you want to run the more capable models that will no longer fit in a single 5090's memory, performance falls off a cliff.
Thank you for this. I wish Apple focused their silicon design on improving the TTFT metrics but coming from an M3 Pro, it still looks laggard compared to Nvidia's TensorCores in the 5090.
Maybe Apple is an acquisition away from changing that balance.
The rumor on Apple's processor roadmap is that they're skipping other M6 variations (all previous generations had Pro and Max, a few had Ultra) in order to focus on the M7 generation for AI reasons. What exactly the M7 improvements are who knows.
https://www.macrumors.com/2026/06/25/2027-macs-m7-chips/
I think that comes down to TSMC. Nvidia apparently booked out the whole A18 or 16 node. Apple is on 2nm right now and M7 will jump right to A14. According to my quick AI research anyway.
That sounds a lot like AI fantasy slop.
Apple just shifted to N2. They’re not going to be doing another major shift right away.
And TSMCs own roadmap would put your hallucination years away at best for a a product that follows a roughly annual cadence https://www.tomshardware.com/tech-industry/semiconductors/ts...
Yeah, Apple has spent 3 years each on the 5nm and 3nm nodes with TSMC. There are some reports [1] that it will jump to 1.4nm after 2 years due to AI but there's no real proof. The source is Digitimes who are frequently wrong with their predictions and rumors.
[1] https://wccftech.com/apple-to-move-to-1-4nm-process-soon-to-...
https://www.tomshardware.com/tech-industry/semiconductors/ap...
M6 got another prompt processing boost. Likely no M6 Ultra though because Apple is reportedly going all in on AI performance in M7 generation.
The selling point of the M5 Ultra Mac Studio is that you can run much larger models that the 5090 can't without swapping. NVidia aggressively segments the market on VRAM for this reason. That's why a 5090 has an MSRP of ~$2k (but good luck getting one for less than $4k) while a 6000 Pro, which is basically a 5090 with 96GB of RAM has now soared beyond $15k where 3-6 months ago it was more like $10-11k. A 6000 Pro has the same memory bandwidth but slightly more CUDA units (IIRC ~24k vs ~21k).
This advantage won't be apparent with a 27B model. The 256GB MS can probably run the newer Flash models locally, something you can't do on a 5090.
I don't think we'll get a successor to the 5090 until late 2028, maybe even 2029. I'm basing this on the launch date of the 5000 series and that we haven't got a midcycle refresh yet. Rumor has it the chips are ready but the 3GB RAM modules are 3-4x the price of the 2GB modules used on the current cards.
Apple should see a Mac Studio major update in 2028. That might even force NVidia's hand. But it's really impossible to say what the state of the market will be 2-3 years from now. It may have completely crashed. I suspect not however.
The interesting thing will be when the bandwidth demands start forcing HBM memory onto these home/enthusiast solutions.
But what about builds that combine 8 of the 5090 with infiniband between boxes? Wouldn't that be comparable to the mac in terms of price and potentially beat it by a lot in terms of performance for the large MoE? I understand the space/heat/noise considerations, but price wise it may still not make as much sense as people think. (Agreed that it is hard to get the NVIDIA hardware and the 6000 pro are priced less competitively).
While that sounds super awesome, How many people are actually going to build and maintain that vs a box you can grab at the mall that fits in a lunchbox?
No, $40K is not comparable to $10K.
When I compare those two numbers, it seems there's $30k of difference
Sounds like nice utility bill in the making.
I can't speak to Infiniband pricing for something like that. It seems like the cheap option is 56/100Gbps with used Enterprise equipment. You'd need 8 HCAs, DAC cabling and a switch but even then you're into thousands of dollars. If you want 200Gbps+ it gets into the tens of thousands (AFAICT).
Each PC is probably going to cost ~$6k and you're talking about 8000W of electricity draw. That's going to consume multiple 20A circuits even at 240V. And the electricity ain't free either. A Mac Studio seems to draw ~500W max.
Oh and the Mac Studio has an upgrade route to run 1T+ models too by chaining them together with TB5 chaining. OSX supports RDMA this way. That's comparable bandwidth to the 100Gbps Infiniband option.
So you're talking about $50-60k of hardware and more power draw and more heat for something that will I'm sure beat the MS M5U option but at huge cost. Also, at that kind of price point, I'm likely to get a workstation PC and put 2 (or possibly 3) 6000 Pros in it.
Why Infiniband ("IB")? If it's for RDMA, that is possible with certain Ethernet cards/chipsets as well. Certainly Mellanox, but Broadcom:
* https://techdocs.broadcom.com/us/en/storage-and-ethernet-con...
and Intel as well:
* https://www.intel.com/content/www/us/en/support/articles/000...
Link level flow control or priority flow control needs to be supported on the switch ports as well.
Others have chimed in on cost and size, I'll chime in on power. The 8x 5090s will require a dedicated datacentre grade power source. The Mac Studio runs on a plain jane wall socket.
Where are you buying 8 5090s for under $10k? With CPU, RAM, and (checks comment) infiniband hardware???
You're probably looking at a lot closer to $60k when all is said and done, and that's before you hire an electrician to run a sub panel for your homelab...
openai make a npu google make npu (tpu no mater) amd buy tellas
every company make his own npu (without xai)
probaby in 2028 we will have more concurent firm on market place
Not forgetting of course that an RTX5090 is what 600W+ ? And the Mac is probably half that at most ?
sure. so is 2x power worth 10x perf? I think it is in most cases.
Certainly not forgetting wattage. A 5090 is 575W. The M5 Ultra Studio is 480W.
nvidia-smi -pl 450 for like a 4% reduction in throughput. I tend to set it around 350W because it's a comfortable temperature blowing on my legs under the desk without warming my office in the summer.
I put together this system two years ago, so it's a little out of date, but it only cost $3000 for the same performance and capability as an Ultra. I don't think I would spend $7000 to save 100W, though.
Yeah people don't pay enough attention to those settings IMO. The first thing I do when I set up a new machine (or upgrade my OS) is to restore all my powersaving configs.
For example I've got all but one of my virtual desktops that put the CPU in powersave mode: I don't need max Ghz when browsing the Web, not even on demand. But when I switch to the virtual desktop where my development environment is, then I want power on demand.
Now I don't do it to save the planet: I do it because I love a quieter computing experience (coupled with Be Quiet! PSU and Noctua fans, this makes for a very quiet computer). That it consumes less electricity is a nice side-benefit.
When you are doing matrix math, compute is compute. Apple cant be more efficient due to physics. The only reason Macs are more efficient in general is that they have tightly bundled hw and sw for specific tasks.
That's a dense model. Of course it will do worse.
Now try running that Qwen 3.8 Next model on the 5090 and tell me what TPS you get (hint: it's near 0 since it doesnt fit the 32GB VRAM on 5090 vs the 256 in OPs M5).
Surprisingly, the Reddit crowd are reporting 50–60 tokens/s (for the 32 GiB 5090 + 128 GiB RAM)—on par with the M5 Ultra benchmarks, despite both the PCIe bottleneck and much smaller DDR5 bandwidth,
https://old.reddit.com/r/LocalLLaMA/comments/1wl06np/qwen38f...
(Note it's a sparse MoE with only 6B active).
Good to know thanks.
That's with CPU offload to a DDR5 6000 RAM though which is around $3-4k at least.
Unlike a 256GB M5 Ultra that is $10k+.
Apple product won't be the cheapest but it is a full package (CPU, RAM, VRAM/GPU, fast-storage, etc).
If you look at the pricing of a full (x86) AI workstation you'd need around the nvidia GPU, you'd approach $10k easily (and be using a ton more wattage too).
I have a 3 year old gaming system. RTX 4080 w/128GB of DDR5. It runs Qwen 38 Flash around 44-40 t/s with 128K context. It is on a specialized build that caches MoE experts and uses an optimized 3bit quant that basically is within a few points of the full 8 bit quant. In general, in casual benchmarking with Alibaba's endpoint I could not tell much of a difference. Overall this model is very good on long horizon agentic work. The main pain point for it is that its input processing speed is slow. Regardless, it gets meaningful work done.
I paid $500 for the RAM in Nov 2023 :)
No wonder Warren Buffet gave up and resigned.
I assume those are non-batched. I think the M series GPU can do 4X to 8X depending on model quant, which means if you can batch queries you'll get almost 4X to 8X performance.
On my m5 max 27b model does 75tps on 256k ctx and starts at 80 on the 8k ctx when you add https://huggingface.co/collections/z-lab/dflash-2 to it. So yeah base might be 30tps (I used iq4) but mtp or dflash help a lot and should be used when checking what is useful and what is not for running models as it is not fare to judge without them.
The next Ultra, supposedly on deck in 2028:
Apple plans to release a base M6 chip this fall for entry-level Macs... a base M7 in the first half of 2027, M7 Pro and M7 Max at the end of 2027, and the M7 Ultra in 2028.
https://www.tomshardware.com/tech-industry/semiconductors/ap...
I really appreciate seeing these dense model numbers. For a large unified memory system though I expect that MoE numbers are what people are more interested in.
These numbers could and should get much better. As an example I can run Qwen3.8-27B-MXFP4 (W4A8) on 2x AMD R9700 that gets 260+ tokens/sec to start and slows down to ~110 tokens/sec over 128k context and can do the max 256k. These are for batch size 1 and throughput goes higher with batching. This is due to speculative decoding, efficient all-reduce inter-gpu compression, and custom GEMM kernels for the specific hardware. Note each R9700 only has 644 GB/s memory bandwidth.
A basic llama-bench on Qwen 3.8 27B UD-Q4_K_M gives pp512 3920 tok/s / tg128 81 tok/s on a 500W RTX PRO 6000 (should be similar speeds to a 5090, chip is basically the same, just less VRAM). With MTP3 this is 140 tok/s on mtp-bench.
This is with llama.cpp. You can of course use vLLM/SGLang well on these cards and they're even faster. On vLLM w/ NVIDIA/Qwen3.8-27B-NVFP4 baseline has a prefill of about 13,000 tok/s. The baseline tok/s is 72 tok/s, but at mtp7, it's 157 tok/s, and w/ dflash7 that goes up to 215 tok/s. On mtp-bench, DFlash2 gets a hair under 300 tok/s w/ the code_python prompt.
On Apple website it says 512GB memory option is available in October. I guess bumping to that one would cost additional 4-6k US$. So an Ultra with 2TB storage would be north of 15k US$.
That’s like 12 years worth of OpenAI Pro subscriptions
Agreed.
Specially since one can pay half right now to OpenAI and sign a 12 year iron clad contract for uninterrupted service delivery of OpenAI Pro.
I hope that was sarcasm.
"iron clad" :)
Censorship included.
I think we all expect the heavy subsidized subscriptions to end or significantly increase in price at some point, but it could be years from now and I'd rather spend a similar figure on an hypotetical Mac Studio M8 Ultra, or whatever more advanced competitor that will have likley appeared by that time.
A more apples-to-apples comparison would be with API cost in OpenRouter at the same tok/s rate for the same models that you can run locally, maybe.
Don't you mean an Apple to NVidea comparison?
Is there evidence that's true though? I mean gross margins on subscriptions being negative since the API is seemingly very profitable (if the price is compared with the cost of serving very large open models).
As long as there is pressure from other providers serving cheaper models that are somewhat competitive without having to incur any of the R&D costs raising prices will be tricky.
HN always has these completely contrived counterarguments. What is actually going to realistically happen that will prevent use of an LLM provider? Did you think that the OP literally meant the 12 years or maybe it was just to show how expensive using a Mac Mini as an alternative is?
Mass revolts of the peasantry burning down data centers and cutting fiber lines.
Yes, it feels like that. Whereas frontier labs are pushing the frontier of human knowledge, selflessly working towards pulling humanity from dark ages. Ignorant peasants trying to burn the modern civilization down. Don't they know data centers and fiber lines are lifeline of modern economy?
I think it goes without saying. And it is eminently evident over last couple of decades that from compute to storage to meals 3rd part providers have saved billions upon billions of dollars to enterprises and individuals alike by providing these essential services.
Yeah, anyone who thinks local AI is going to save them money is likely to be disappointed, at least if they want to run models that are even remotely capable.
Plenty of other reasons to get excited about local AI, but I don't think cost is one of them.
Maybe you are using a local model to go after some Millennium Prize problem and you don't want OpenAI to take your work and use it to win the prize for themselves? $15k might be a bargain.
And, yes, I know a current local model wasn't going to solve the Navier-Stokes problem, but I'm just using it as an example where privacy might be valuable.
Agreed, plenty of other reasons to get excited about local AI.
I'll try that argument with my wife next time I want to buy a $15k mac.
It's a bold strategy cotton, lets see if it pays off for em.
Despite being on a site called Hacker News, we seem to often overlook the simple aspect of wanting local AI hardware to hack (not necessarily in the cybersecurity sense) with. I got my local AI hardware because it's an enjoyable hobby for me.
apparently if you ever point out HN starts for hackernews and thus expect related attitudes you get downvoted by shocked ( what I guess are zoomers and not bots ) that desperately opine the name is a random abberation doesn't mean anything and one should not deviate from our corporate overlods in any manner.
A lot of people commenting on how bad idea buying local hardware for inference is also miss the fact that even in 3 years that hardware gonna cost something.
Might be if RAM prices get much more reasonable its gonna be 1/3 of the price, but it's very much possible its gonna be half or more.
And if you're buing Mac Studio and not some AI-only locked down board it's possible to reuse it for other purposes.
On a personal level maybe not yet, but for a medium business upwards it may make sense.
Hard to guess, it can go either way. If you will need to be in a syndicate to use non-sterilized models, that mac makes sense. But if there is mandatory registration of personal cyberarms, you risk going to mines once they check you purchases. You could try to play normie and pretend you simply wanted to show off, by keeping your actual work on external disk, but that leaves traces on system. Counting on someone in the Gap renting you gray iron works as long as you can swap credits. Still, this gear is tiny. Put it in your e-car, with uplink, and leave it at uncle's farm. Discreet.
I gotta have some of what you had :)
I thought it was a fun bit of cyberpunk fiction. Those who downvoted him seem to have taken it at face value?
I appreciate the reference to RUSH: Red Barchetta in the final line.
It was a dark rainy night in Neo-Tokyo as Blake puffed on his vapor cartridge and watched the Mac dealers prowl below. Almost 15k Union Credits to get one of them to meet you in an e-cafe with a fully loaded M5, but man, the inference rush from one of those things was something else.
I'm sold on "personal cyberarms" as a concept
Do they include footguns from pointer bugs?
Just commenting here because you're discussing hardware: I thought the test results from the SSD published in this article [1] were pretty interesting. Maybe that's old news though.
[1] https://www.macworld.com/article/3238319/mac-studio-m5-max-r...
512 option isnt worth it imo, you get severe slowdowns when weights are that large. 256 is the sweet spot, you can run large open weight models at decent speeds for full private inference.
I think most people are getting 512 for running Chrome with a bunch of tabs open. /s
a) we don't actually know what the prices will look like yet, b) what about same weights + huge context? or, same weights that you'd run on 128gb/256gb, but multiple models running for different tasks?
I think it's safe to start the conversation as about bad as the jump from 256 to 512 on the M3, which was a little more than double base to 256. If it's surprisingly different at launch then it can be a party, but there is no sense getting your hopes up for that at the moment.
Longer context also slows token prediction proportional to the context size. If it wasn't regularly referenced then there would be no need to keep it in RAM.
Usually the pitch for more memory is "I can run a massive model/context and get my answer in a while instead of next weekend from disk".
The model being tested is 18k as configured.
I didn't expect this to make the 5090 to look like a good deal.
5090 has 32GB VRAM.
It'd be silly to buy the 18k model to run a tiny model like Qwen 27B. You use models like GLM Flash and Qwen Next which won't fit on a single 5090.
Is it that silly? You could run multiple 27B models in parallel.
You actually don't need more RAM to batch multiple inference tasks of the same model.
(Each task needs its own context, but the (e.g.) 27B of constant parameters isn't duplicated).
You definitely need more RAM if you are not satisfied with small context windows, especially if the weights take a large % of the total memory to boot.
can run multiple subagents of Qwen 27B though, right? Unless I am fundamentally misunderstanding how VRAM constraints work
You might be. Running another agent doesn't load a set of new weights. It creates a new KV cache for the agent and adds the prompts to the queue. Its just another inference turn.
Yes Apple has some of the best hardware out there, albeit overpriced. But the software is such a hindrance and I can't take anyone that states otherwise seriously. If only it had proper Linux support (and the Asahi people do an amazing job but you can reverse-engineer only so many stuff with limited funding, and then you have to do it again for new models). MacOS is good if you just want to have a standard experience, which to be fair is most people. It's good for just setting up an LLM server I guess since the hardware is a perfect fit. I wouldn't touch it otherwise.
What exactly is missing from macOS that makes you feel the need for Linux?
I get it on Windows systems, at least when someone wants to use Linux-type tooling. But macOS already supports pretty much all of that natively?
For starters, the source code.
And why would you need that to run LLMs?
Apart from that, for the UNIX part, the source is available for quite a few components:
https://github.com/apple-oss-distributions
notably also the kernel
https://github.com/apple-oss-distributions/xnu
Strictly speaking, Apple can claim to ship a UNIX® operating system:
* https://www.opengroup.org/openbrand/register/apple.htm
* https://www.opengroup.org/openbrand/register/
Yeah for a while they used that in marketing copy actually, but it's been a while I think.
kokonokko1337 already said it was good enough to run LLMs, presumably RunSet isn't saying the source code is needed to run an inference server.
They say, not doing anything with Linux’s source code other than mentioning its existence.
Comparing to Linux is too general. You need to say what distro and what install level. I really only use headless Linux, but I've never logged into a fresh install and not had to do some sort of 'apt install devel-packages' equivalent for which ever distro being used. That's the same thing with macOS after choosing which package manager to use. I don't see how Linux vs macOS is very different
This is great as a first look, but the author is not a developer, so we don't yet know whether a dev can be as productive with local models on M5 Mac Studio compared to a 20x subscription plan.
I'm also curious about any new low hanging optimization opportunities in the kernels for this new hardware.
It's already clear to me that M5 Mac Studio is more cost-effective than anything you can run on open router, assuming decent utilization.
The M5 Mac Studio will be the most cost effective way to run uncensored cyber capable open agents.
An exciting tipping point will be if programmers can get an Astra-Ultra like experience all week with this hardware. That would be a real sense where this hardware exceeds the value of even 20x cloud subscriptions.
Local models are definitely not as productive as SOTA, sadly it's not close yet. I do think someday they will be "good enough" to use, but they aren't today. Even the SOTA models barely code well, with Opus 4.5 being the first, good coding model.
That being said, I think it's absolutely imperative that we keep pushing local model performance. We need to continue to advance technology there and ensure that the model labs don't do regulatory capture in the name of "safety" (or anything else).
With the latest codex (weekly quota burn) fiasco I tried open weight alternatives for the first time. And tyeah... open weight models cant compete with likes of astra yet. But, my hope is that by the time I get my Mac studio at end of november an open weight models would have closed the gap (which i think is realistic at the speed of progress). Now its true a better gpt version will also be available then but it also seems the gap is shrinking with time so theres that.
I think this is true, but also misses that a lot of us are just doing basic flask apps with a react front end. We don't need astra; Something sonnet 4.6 level locally is perfectly sufficient 95% of the time, and maybe 99% of the time.
This. People have convinced themselves that the absolute frontier is what is needed, anything below it is an unacceptable compromise, and we seem to be speaking different languages when it comes to discussing model capability.
It's like watching a discussion about cars available to take on a 100km road trip. A new car gets released that is on par with a Toyota Corolla but it is dismissed as completely useless for a 100km trip because it doesn't have the seat massagers and air ride suspension that the new Escalades have.
The reality is that something like Sonnet 4.6 is still amazingly capable for so many programming tasks, especially if you already have some reasonable level of experience to steer it in the right direction.
And if you think Sonnet 4.6 is still worthwhile, then it seems undeniable that something like Qwen 3.8-27B is also worthwhile.
Local models can be widely used as productive assets. Yes the infrastructure of SOTA API models is engineered specifically for you to be that utility, but the blanket statement that local isn't up to par is intensely short sighted. Billions of tokens per month on local pays for the hardware when compared to sota costs per month.
I believe they can currently be used productively for non-coding tasks (classification, light summary)... but they definitely are not even close to SOTA when it comes to software development.
Defining productivity is a use-case scenario, and a wildly generalized assumption for most people in this argument. Local infrastructure doesn't need to be sota for absolutely every single need for a dev lab, but it absolutely can be delivered with non-api frontier class models.
Just to be clear, I'm specifically talking about coding. I think local models can help with productivity today, just not coding.
I'm also a huge fan of local models and think it's absolutely imperative that they continue to advance so we can move off of the Anthropic/OpenAI hosted models. It's important to accurately asses where we are in that journey though.
I think the issue is generalization, if you were more specific about which local models aren’t good enough for which tasks compared to which frontier models in your experience, it’d be a lot more informative
I can't just go into any codebase and ask a local model to "Implement this feature: xxx" and get acceptable output. I hope to someday soon though!
Like the other commenter, I'm confused about the 'just not coding' conclusion. I'm using Qwen 27B on a 5090 at > 100tk/s with 150k context (which isn't enough admittedly), and DeepSeek v4 Flash with 1million context on a gb10/spark. Both of which are performing surface level, and deep needle precision infrastructure architecture. They code 24-7, stupendously.
It would be interesting to hear more about how you’re actually using them. Do you have sophisticated feedback loops around the models so they can verify their work and converge on good solutions? And how do you decide what to give the 5090 vs the Spark vs a frontier model?
Correctness matters much more than speed to me, but if I can get both, that’s obviously very interesting.
Local models are undeniably capable of "helping with coding" today.
Astra-Ultra? Even the largest open model to date (Kimi K3) is nowhere close to Astra level, and it will be quite slow even on the highest-spec M5 Ultra, with achievable speeds of about 0.5 tok/s at most due to having to stream weights from SSD (~13 GB/s on the highest storage capacity M5 Max machines so far). This is OK for doing simple Q&A in the background but it's far from a genuine coding experience. You'd have to test batching of multiple thinking streams in order to try and raise overall tok/s via layer-wise reuse of the streamed weights (and this is where the "Ultra" part sort of becomes relevant; Kimi series models have good support for agent swarms) but this would decrease single-session performance even further. It would only be usable for background jobs, though the hardware would then have a chance of paying for itself if it was fully used on a 24/7 basis.
isn’t this very straightforward to do..? I thought batching for Qwen models is already proven out.
Well let’s take Qwen 3.8 27B. Throughput for M3 at 8 agents is 4x compared to single agent. [1]
It’s really not clear to me that 8 concurrent agents at half speed will be worse task completion latency than 1 agent.
And that’s M3 studio benchmarks, not even M5 ultra, and without the many software improvements we will see
If you haven’t tried Qwen 3.8 27B xhigh on a task you might not get the hype. Idk.
If you’ve tried doing this and don’t like it sure, and be specific about what isn’t effective, but let’s not speculate.
[1]: https://omlx.ai/benchmarks/performance/69kzkrv8?utm_source=c...
That's all well and good but Qwen 27B is a small, dense model; that's favorable to both batching and MTP. Batching of large, sparse/MoE models like Kimi K3 (requiring slow SSD streaming even on a single maxed out Mac Studio) on local hardware is an entirely different game that's mostly theoretical so far: many people would even call it outright pointless. (MTP clearly fares even worse, though - unlike batching, it ends up wasting scarce weights-fetching throughput on wrongly predicted tokens.)
Ehh, the actual elephant in the room is:
"why bother with local AI at all when you can lease a GPU for $5/hr?"
To which the answer is you shouldn't bother, unless you have a bunch of money to throw at hobby projects.
there are lots of people with very expensive hobbies, see sailboat racing for example.
$5/hr = $3600/mo.
Unless you only need the AI available some of the time, $5/hr is pretty expensive. That's an RTX Pro twice a year.
If you're using it for discrete sessions of coding or something, that might make sense for you but if you're using it for an always-on assistant, that pricing kinda sucks.
I would imagine extremely few people are utilizing an H200 for every hour of a month. Especially for something like an assistant
5090's are like $0.20/hr
While I know it's not apples to apples, the target comparison right now is 2x DGX Sparks. Similar price, 256gb. The conversation has focused on memory bandwidth vs. compute in agentic loops, so for most people the raw numbers will mean less than the "time per task" in coding benchmarks.
This is a great article and bodes well for the M5, but we should expect more like this comparing to other platforms before we truly understand where it fits.
Speed vs task-completion is a new conversation and a great point. Whereas the cost to compute doesn't exist in a vacuum, making mistakes costs less, is easier to maintain with granularity and a whole host of other factors when you own the lab.
Imagine spending a trillion dollars on data centers and then reading this article. Nightmare fuel for OpenAI
For 99.99% of people, spending 15 grand on a Mac Studio just to run Qwen 3.8 locally is a non starter.
It's not the M5 Ultra itself, but the M7s or M9s that will do the damage.
99% of people will use whatever AI is free. The sophisticated, heavy users that are willing and able to pay a lot of money the ones that will be interested in controlling their inference bills.
Today, the sweet spot where an M5 Ultra makes sense is tiny. But we might expect that to grow a lot.
Anthropic is reporting 100 Billion ARR.
Even if you could get a frontier model, you would not be able to run it on any Mac. So speculating on what M7 or M9 will achieve in 5 years (if we even still exist) seems pointless.
Do you need a frontier model to write emails, check your calendar, search the web?
I don’t think Apple is going to lie down and cede AI to the cloud.
How about writing mail to President and senators on AI doomsday scenario if frontier labs do not pace themselves?
That mini model on mac mini would scared to hell to do such thing. It need that rugged frontier model to speak truth to power.
True, but do you need a 10,000 dollar machine to do so?
I would love to find an excuse to buy a 10,000 dollar machine! But I cant find one yet. My current cloud bill is in excess of 400 USD per month. Just can't achieve frontier model capabilities locally.
at 15 tok/s
Yes, but in 7 years?
In 7 years we will probably all be living under ground fighting skynet with plasma rifles made out of old microwave parts
You will. Some of us are going to be already ground into dust that the microwave parts are made out of. Others will be locked into our communism cubes with our daily allotment of entertainment and sustinece. let out into the sunlight for only 30 minutes per day.
I’ve watched Terminator 2 extensively. I am prepared
And nightmare fuel is just what they'll be selling at the UN this week, for this very reason.
Sam's address will probably be more riveting, imaginative, and terrifying than the last couple of Terminator screenplays. Legislators will lobby him to write the laws for them, and the ghost of Harlan Ellison will threaten to sue him.
I don't see how that math works? This is a $15k rig under benchmark and per the results it competes very acceptably against... one consumer GPU.
I really don't see who buys this, except people who want the Studio for some other reason. But nothing in the story says you want to fill racks with these instead of Blackwell or TPU parts; it's not even close.
Your math is correct, but it’s math based on today’s economics.
Think of a company like Apple moving onto your turf. They’re not going to cede AI to the cloud. They want their part of the pie.
So in 7 years, how much AI will be handled locally on your iPhone. And will you have repaid all the debt on your balance sheet before Apple eats your lunch
I think most important thing is that Nvidia doesnt want to give 100% of market to frontier AI labs either.
It's way too easy for 1T+ frontier labs to ditch Nvidia. So Nvidia will also put effort to make sure there are open weights models and local hardware available.
And Apple will benefit from this too.
There is zero chance that an LLM approximating a modern frontier model is going to be running on a phone in the next decade. Even if you grant that you could stack enough DRAM dies on top of each other in the package, that would be a three order of magnitude improvement in power efficiency just for the compute.
This dream machine costs over $15k (not including the Apple Studio Display)? Nah, that dream is SO OUT OF TOUCH!
The irony here is, thats hobby hardware. You spend 20k and can run slow hobby models that are barely capable of anything unsupervised.
Entry level serious hardware starts at 100k, and a bit better but still almost-useful grade is 200k (8x rtx pro, plus a nice epyc pairing). Thats the sort of thing a salaried expert lets their employer buy them for sort of serious work.
Anything really serious is well north of 1M - not including the housing and commercial grade mains connection. And at best that buys fast Kimi K3 or GLM.
If you are buying expensive hardware to run LLMs "on your own machine" you will soon find your ladder is on the wrong wall.
Ordered one for OpenFOAM. Excited for it. Will be nice to not have my laptop running CFD 24 hours a day, but my M1 Max is currently my fastest machine… I’m expecting about 3.5x from the M5 Ultra.
I think Apple is really sleeping on making this run a Linux server. These things are very capable and draw very little wattage when idle. It would make an excellent homelab device, but MacOS currently holds it back in this regard.
https://mac.getutm.app/
i use linux a ton too, but still wonder what you want in a Linux server that macos as a BSD server does not have.
I'll probably manage with Mac OS well enough, but my linux distro comes out of the box with all the latest OSS tooling I'm familiar with, plus a package manager, and it has linux cgroups and namespaces that power the container technologies we all know and love.
If I switch to Mac OS, I have to sort out a package manager and install all the stuff that's missing, and when it comes to containers... they're just linux VMs. I'd happily cut out the weird proprietary middleman if I could.
My mac is 5 years old. I don't think I can comfortably buy a new one right now. It has a 16GB unified RAM. Honestly that would be enough for so many local models that I want to use but can't use. Because RAM usage (even with literally every single user installed app quit/stopped) the RAM usage is very high that I can barely safely get 6-7 GB (I am supposed to get ~10 GB, but it goes up and down real fast!). That's a shame. If only I could install an alternative OS that uses very little amount of RAM :-)
How are you measuring memory usage? top tells me that 45/48GB is "used", but Activity Monitor shows me that 24GB is cached files.
I'd be quite surprised if Mac OS alone needs more than 8GB, given that they sell the Neo with 8GB of RAM today.
Free RAM is wasted RAM.
When people benchmark MLX related quant models, they really need to publish numbers on benchmarks. You cannot take this as it is what you get of the original models. MLX uses pretty simple quantization methods so at lower bits without QAT, it is just not as good quality as llama.cpp ones.
At this point I think I will get the DGX gb300 workstation though I will wait a bit more for the cold season. It is double the price but at least is the real thing
Double?? They are nearly $100k.
The Year Of Local AI will be here no later than 2040, coinciding with the Year Of The Linux Desktop.
The year of linux on the Desktop was 26 years ago for me.
"a total cost of $0" Uhh ... how much is that hardware?
Another comment approximates at around USD$15k, so yeah, not zero.
For HN readers earning that sweet sweet VC money, that is zero!
I think the power itself will probably be more than you're paying in a subscription
[delayed]
Lol, try generation of images & videos on these, they ought to improve perf on Deep learning not just llms
What are good options to run local models nowadays? Something good for coding and personal assistant kind of things
I think we'll eventually get to the point where folks will have a local AI agent but I think people need to temper their expectations to a degree. You aren't going to have data center level tok/s from a box sitting under your desk and you don't need instantaneous responses for many workloads. Having a local agent that can execute tasks over a couple days with your supervision that might otherwise take you weeks is perfectly acceptable.
However I also think that Agentic AI is very much not an out-of-the-box solution, local or otherwise, and it takes a high level of technical knowledge to create an effective AI agent. And there's a problem now where most orchestration is fixed on what models are used for what tasks with no ability to weight constraints like cost, speed, and security.
Fantastic review, this is how it should be done with local AI.
i can buy a house with that amount of money. it used to be car money.
Once you hit the memory you need, generation speed is mainly set by bandwidth, and every Ultra from M1 thru M3 has ~800 GB/s. IMO best ROI for most people is 'cheapest used Ultra with enough RAM'
I setup an eBay alert and picked up a used M2 Ultra that has delivered good ROI (at least, far better than 15k for comparable-for-my-use-case performance)
This one is 2x m5 max, so ~1.2TB/sec.
I think M1 through M3 were compute bottlenecked in prompt processing (hence the very large gap between M3 and M5, in this page's benchmarks, that's not explained by memory bandwidth alone).
For generation speed in isolation, yes.
The M5 generation added tensor instructions to the GPU cores.
Everyone looking at the Qwen3 27B model and the 5090. It’s like saying a Porsche is better than a $5m Komatsu earth mover at moving a 20lb carry-on suitcase. Yes. Yes it is. Why do people spend $5m on a komatsu then, when this one metric shows the Porsche is better? Huh. Show us the “Moving 300 metric tonnes in one load” metric. How’s the Porsche now? Oh, the Porsche is in the Komatsu? Ok I’m getting a bit carried away with that analogy.
Waiting for my 64GB M5 Pro Mini, hoping it will also be fun to tinker with for local AI
I find it funny that the thing always mentioned with this machine is local AI. If you're a local model enthusiast, then maybe that makes sense, but I just don't see the economics working there.
I ordered the same one for work so I could run more local agents at once (many iOS simulators and Xcode build processes).
What do people realistically do with these? It's too slow and du... not SOTA-level for coding. It's way too slow for video. I tried simulating an "Fable herding Qwen subagents" and it takes much longer and delivers a much worse result than Fable/Astra alone.