This isn't a good analysis, and it's because it keeps rounding everything up. He rounds up the cost of electricity by 10%. He has a range of power use, takes the high end (which is 2x the low end) and multiplies it by the inflated electricity cost.

But then they talk about using a newly purchased Mac to do the inference, running at full capacity, 24/7. Why would you do that? Apple silicon is fast but the author points out: you're only getting 10-40 tokens per second. It's not bad, but it's not meant for this!

It's comparing apples to oranges. Yeah, data centers don't pay residential electricity rates. Data centers use chips that are power efficient. Data centers use chips that aren't designed to be a Mac.

Apple silicon works out pretty good if you're not burning tokens 24/7/365 and you're not buying hardware specifically to do it. I use my Mac Studio a few times a week for things that I need it for, but I can run ollama on it over the tailnet "for free". The economics work when I'm not trying to make my Mac Studio behave like a H100 cluster with liquid cooling. Which should come as no surprise to anyone: more tokens per watt on hardware that's multi tenant with cheap electricity will pretty much always win.

Unless I'm misunderstanding, this is counting the entire laptop in the cost of generating tokens. The calculation seems to omit that, in addition to receiving LLM output, you have also received a laptop in exchange for your money. If you intend to put this machine in a dark corner and run it solely as a token-munching server, a laptop would be an exceptionally poor choice of technology for this purpose. But if you intend to use the laptop as a laptop, having a laptop is a pretty big benefit over not having a laptop.

You also get the benefit of privacy, freedom from censorship, and control over the model used (i.e. it will not be rugpulled on you in three months after you've built a workflow around a specific model's idiosyncrasies).

Frontier AI companies are selling at a loss.

Excusing everything else that u/bastawhiz said[0]; the obvious fact here is that Claude, OpenAI, Gemini et al. are quite literally burning through 100's of billions of dollars and selling it back to you for pennies on the dollar in the hopes that they get to be the only one left.

If I spend $10 growing Oranges and sell them to you for $1; then of course it's more expensive for you to do the growing.

I feel like I'm taking crazy pills. These models will become more expensive over time, it's functionally impossible for them not to, they just want to capture the market before they have to stop selling at a huge loss.

[0]: https://news.ycombinator.com/item?id=48168433

If you want a good dense model, use qwen3.6 27B instead, speed will be up, and if you don't take my word for it being smarter, take openrouter's prices of it against the bigger, slower and less memory-efficient gemma do the talking.

If you want a faster model, go for qwen3.6 35B (or gemma 4 26B if gemma models perform better for your tasks). There is a reason why people (myself included) haven't shut up about those two (especially the 27B). Its small enough to run at a decent speed (especially with the built in MTP that finally has official llama.cpp support) and for many workloads (every benchmark I have ever thrown at it) it is matching or surpassing models it has no right to.

A couple of days ago I woke up with my internet being down, started 27B in pi, told it to diagnose whats wrong by giving it my router's password, went to grab a coffee and by the time I got back, i had a full report with suggestion on how to proceed. I love openrouter and I use it for many things, but it is not cheaper.

Subjectivity and opinions based on personal experience with all those models implied naturally, I assume the 31B gemma has cases in which it edges out, I've just failed finding any and I have been running all 4 models mentioned since hours after each of them dropped nonstop for different tasks. Hell, for my hermes, I've started getting better results once I switched from gemma 4 26B to qwen3.5 9B, not even the massively improved 3.6 series. It just feels outdated/ cherrypicked to not use what by many accounts is the current consumer hardware SOTA if doing such an analysis.

A lot of comments here are about the issues with the analysis in OP’s post but much of them are “a distinction without a difference” with respect to the broader conclusion. When we look at purely cost and performance (setting aside privacy) then it’s better for individual devs to pay for hosted then for self hosting. Employers are paying for tokens on the job and most devs are finding the $PREFERRED_PROVIDER’s $20/$100/$200/month subscription sufficient outside of work. Most devs don’t fall in the conditions under which running local models make sense purely on the basis of cost vs performance.

More critically, in practice, setting up local models seems more like a hobby, an educational exercise, or an act of privacy control than it is for cost cutting or productivity.

OP is comparing against Gemma everywhere but concludes paying Anthropic make more sense. Anthropic is $15 per million output token which is 30-35x more expensive even in openrouter .

This is like comparing e-bike at home with e-bike rental and concluding therefore we need to rent Toyota since it can go at similar speeds. Getting tired of bad posts getting much attention .

Mmmm, nope if you do the smart thing. MacBook M5 max 128gb is a premium laptop at 6k, but with it you can do many things and is your good main driver for the day. Then, it can also run DeepSeek V4 flash and perform non trivial tasks locally, without censorship or limitations, even without an internet connection and on very privacy sensitive data. That's a good deal. If you buy 25k for a dual Mac Studio 512gb to abandon OpenAI and company you are going to be disappointed by both performance and cost.

The author only compared output token costs -- but for typical agentic workloads, input tokens dominate the costs by a large margin. Running inference locally, input tokens are, to first order, free. (They only generate implicit costs through higher time-to-first-token, higher power use, and lower token output speed).

In my testing, qwen-3.6-27b in full precision is well below sonnet, but above claude haiku in coding tasks. Gemma is not even close to qwen, it’s much, much worse.

Article is seriously wrong, because it makes a huge mistake in the last part. You can't simply look at the produced tokens and that is your cost. In agentic coding there are lots of turns meaning you not only pay for the output tokens you also pay for all the input tokens sent each time (even if a lot cheaper, like 10x when cached). So this calculation does not accurately represent the api cost at all.

Second thing is you can starkly upgrade the token generation locally if you use agent teams. Single conversations are memory bandwidth bound and don't fully make use of your compute. If you can batch tokens from multiple agents you can easily 5x token generation.

I simply can't go back to cloud AI. Privacy and full control are more important to me than speed and SOTA models.

"Accelerated depreciation (if any) from shortening the lifespan of the device will be more expensive than the electricity"

Shortening the lifespan?

How much does your data privacy cost?

Right now, local inference only make sense for privacy reasons.

This is common when processing PII. Lawyers, doctors our similar should not be using cloud solutions.

Also it's harder to setup and always more expensive than any cloud solution.

Author forgot that after 3 years when hardware no longer decent for inference you can still resell it for 25-50% of price.

Obviously if RAM apocalypse passes by then high-end configurations preserve resale value worse than base models, but still it's hefty bonus of Apple hardware that might change math a lot.

I like that the numbers were crunched, but the answer to these is always a bit of a foregone conclusion.

* Industrial power pricing

* Wholesale hardware pricing

* Utilization density of shared API

means API always wins a cost shootout.

Privacy & tinkering is cool too though

Slightly different slice into this a very similar situation (local vs OpenRouter AI inference).

But in _every_ metric other than privacy it was better to run via OpenRouter than a local model, and not by a small amount.

Direct link to the comparison charts:

https://sendcheckit.com/blog/ai-powered-subject-line-alterna...

Local LLMs aren’t about cost, but control.

I don't hear people debating which is cheaper, local or cloud run models. The conversation, at least what I hear, is a lot of the time users are not utilizing an awful lot of tickets all the time, those providers will be paid if you never use them. If 80% - 90% of the work I and my team are doing with Ai is grunt work, write tests for this, implement a FFT here, write the dB query for X. Nothing exhausting. Those who are using AI for whole cloth "vibe coded" applications and services are definitely better suited to cloud. If a work laptop can run my local models and get my works needed performance for development, why wouldn't I as a company prefer that?

Add to that the privacy improvements and data protection and potentially further specific inferance if needed it's a no brainer.

Again, Ai is a tool, and the right tool for the job, I would wager with no evidence looked up, is that the majority of Devs would be happy with 10-30 per second locally.

So I did the India-specific analysis for a tier-3 city. Here, electricity costs 1/3rd of the US version, and you also get solar subsidy up to a certain amount.

https://shorturl.at/q6gRE

tldr;

Hardware deprecation costs are the major factor.

But, if we assume ZERO hardware deprecation (not realistic), then local inference becomes super cheap.. roughly, 90%+ cheaper.

Third case: the break-even happens only if we can get at the very very very least, 8.7 years of useful hardware life. A more realistic number, however, when working 8 hrs/day and not of 24 hrs/day, is around 25 years.

So, for now, local inference is preferable if you deeply care about privacy. From cost perspective, it's still not there.

> "run a model like Gemma 4 31b, which is almost anthropic sonnet levels of performance"

I wish people stopped deluding themselves — I regularly try (and benchmark for my purposes) local models and they are NOWHERE near the huge models like Sonnet or Opus. Nowhere. Yes, you can sometimes get plausibly-looking output for simple tasks, but for anything even remotely requiring thinking there is simply no comparison.

Local models are useful. I use them for spam filtering, and soon intend to use them for image tagging and OCR. But let's stop saying they can get us "anthropic sonnet levels of performance", because that's just not true.

For me, the appeal of local compute is first and foremost confidentiality and having the possibility to run my 200K documents through an LLM just to see what happen without having to consider the cost.

Consider deepseek as well. About 50 cents per 1M tokens, for >1T model

It should not at all be surprising that running models at home is more expensive than commodity providers. That's just generally true of running your own stuff. Even if the cost in money isn't higher, the cost in time is often _significantly_ higher.

This is why the idea that the AI labs are in trouble because inference will be a commodity is _completely backwards_. Some of the largest and most powerful companies in the world sell commodities. They compete on scale and efficiency, and you are never going to be able to compete with the big labs on either.

Except I already have a local Mac to run Xcode. OpenRouter cannot help with that, at any price.

> 64 gigs should run a model like Gemma 4 31b

No, it can run anything in the 70B range. It's a notable quality upgrade from the 30B, which isn't obvious because the famous flurry of April releases didn't contain any 70Bs.

It can also run 120B in UD-Q3. Or 230B disk-streamed.

I've dug into this previously for one simple reason: NVidia segments the market by capping VRAM and Apple silicon uses a shared memory model that could challenge that but it currently doesn't. And I really wonder if Apple realizes the potential of what they have or if they even care.

So, for comparison, a 5090 has 32GB of VRAM and you can get one for ~$3000 maybe. To go beyond that memory with current generation (ie Blackwell) GPUs, you have to go to the RTX 6000 Pro w/ 96GB of VRAM and that's almost $10,000 for the GPU by itself. Beyond that you're in the H100/H200 GPUs and you're talking much bigger money.

Part of the problem here is the author is looking at laptops. That's the only place you'll find the M5 Max currently. The real problem here is that the Mac Studios haven't been updated in almost 2 years. There were configs of those with 256/512GB of RAM but they've been discontinued, possibly because of the RAM shortage and possibly because of they're reaching EOL. Apple hasn't said why. They never do.

Many expect M5 Ultra Mac Studios in Q3 and the M5 Ultra may well have >1TB/s of memory bandwidth (for comparison, the 5090 is 1.8TB/s). Memory bandwidth isn't the only issue. A 5090 will still have more compute power (most likely) but being able to run large models without going to a $10k+ GPU could be huge.

But yes, it's hard to compete with the scales and discounted electricity of a data center. Even H200 compute hours are kinda cheap if you consider the capital cost of what you're using.

I've looked into getting a 128GB M5 Max 16" MBP. That retails for $6k. You might be able to get it for $5400. But I don't think the value proposition is quite there yet. It's close though.

> Let's round up to $0.20 per kWh.

Next paragraph

> At ~50-100 watts and $0.18/kWh that's $0.009 or $0.018 per hour. $0.02 per hour. $0.48 cents per day for the electricity to be running inference at 100%.

lol

Apple services are ~27% of revenue and growing double-digits. The chip is a moat for that flywheel, not a standalone compute bet.

Will this cost structure always be this way and are there other benefits to not running your LLM on the cloud?

E.g.

Privacy

Uptime

Future cost structure controls

This is a field that has moved very quickly. And it has moved in a direction to try to trap users into certain habits. But these habits might not best align with what best benefits end users today or some time in the future.

Wouldn’t a Mac Mini be a better comparison?

The true advantage of locally self-hostable, open weight models isn't about monetary cost at all, it's about the CIA triad.

Running locally, you get confidentiality of knowing your tokens are only ever being processed by your own hardware. You get the integrity of knowing your model isn't being secretly or silently quantized differently behind the scenes, or having it's weights updated in ways you don't want. And you get the availability of never having to worry about an API outage, or even an internet outage, for local inference capacity.

And this isn't even starting to address the whole added world of features and tunability you get when you control the inference stack. Sampling parameters, caching mechanisms, interpretability etc.

OpenRouter may be cheaper than frontier labs, but you still lose all of these benefits from open weight models the moment you decide to rely on someone else's hardware for your processing.

Your laptop AI costs too much? Speculative investors can help!

I'm even surprised people ignorantly talking about advantages of buying very expensive device , run it only sometimes and aiming to beat cloud vendors.

If small model is great it will be hosted with good electricity cost and will be utilized 24/7.

Isn't it 2+2 of economics ?

CPU is a commodity, and we are still buying cpu and ram from vendors for same reason

What would really elevate an article like this is if we could somehow quantify human brain’s equivalent outputs and compare the costs with local LLM and cloud LLMs.

Open router doesn't cost money per say, it depends on the providers pricing

I think that the main flaw in the reasoning is assuming that cost of token will stay the same over the years.

Chances are that token prices will go down, but chances also are that the AI bubble pops and all of a sudden all these companies will either have to make a buck out of the inference or go bankrupt.

Getting your own hardware just grants you stable pricing.

Bizarre running local models have nothing to do with cost. It's about privacy first and foremost

Local isn’t (just) about cost, it’s control and trust.

Now include the externalized cost in the U.S. of deploying ~100% of productive capital to build data centers instead of, for example, first-world transportation infrastructure, and tell me which one is cheaper

OpenRouter and other LLM platforms are being subsidized by VC investment to less than it costs them to run inference, the MacBook Pro is not

OpenRouter doesn't expose all the LLM sampling parameters/research that llamacpp, vllm, sglang, et al expose (so no high temperature/highly diverse outputs). Also OpenRouter doesn't let you use steering vectors or LoRA or other personalization techniques per-request. Also no true guarantees of ZDR/privacy/data sovereignty.

Oh, and the author didn't mention at all anything related to inference optimization, so no idea if they even know about or enabled things like speculative decoding, optimized attention backends, quantization, etc.

At least AI slop would have hit on far more of the things I listed above. This is worse-than-AI.

The full-amortization framing is doing a lot of work here. I bought my laptop because I needed a laptop, not as an inference box, and running a model on it is incidental to that. Once the hardware is sunk for other reasons, the only cost left is electricity plus whatever depreciation you accelerate by hammering the SoC, which the post actually acknowledges in one parenthetical before allocating the full $4299 to tokens anyway.

Also nobody I know picks local over OpenRouter on price. They pick it for offline, for data not leaving the machine, for no rate limits, for not having a provider go down mid-task. If $/Mtok is the only axis, sure, cloud wins.

In practice the pattern I see is leaving a small model running on easy background tasks while using the laptop normally, not a dedicated inference box hammered flat out for 5 years.