No mention of the performance of the models? I'm able to load a bunch of different models on my little mini-PC with 16GB RAM, but the performance is terrible. I always wonder what performance people are getting with local models that they find is acceptable?
I run a similar setup to the one he described on similar hardware. I run bifrost and llama swap though (tailscale rocks). My local model usage is for some out of band batch processing one of my personal apps uses. Basically a personalized recommender for media, it curates stuff for me based on a database i've compiled over years, so non-interactive. For that use case, I don't really care that it might take a few minutes to run. It's free. The machine is just sitting there anyway. I have tried using qwen-coder and opencode on my M5 Max 128gb and compared to claude code it's painful. I did setup a workflow where claude plans, qwen executes (unattended overnight, again b/c it's slow) and then claude reviews. I benchmarked this several times and I ended up using MORE tokens with claude because it had to 'fix' all the qwen issues. While the code it produced was 'good enough' the fixes were worth it so I just stick to coding task using API models (codex and claude).
Can you share a bit more about your bifrost and llama swap setup? I’m facing memory constraints and am looking for a managed model solution that will help with hot swapping loaded models and stay-warm concurrency. Ideally with prioritization.
What do you want to know? Just start llama-swap with the models i have downloaded, add llama-swap as a provider in bifrost, expose the models you want and they become available in one single endpoint you can use in anything like opencode, openwebui or anything that speaks openai.
i have a 512gb ram m3 ultra mac studio setup with a gas city that runs one of my companies. today was the first time ever that a local model (GLM5.3 8-bit) was able to match fable5 in our tests.
GLM-5.3-Flash at true 8-bit: 341 GB on disk, 328 GB resident, 288 experts across 46 layers, loads in 65 seconds. • 18.7 tokens/s generation, 35 tokens/s prompt, on a desk, on a $0 per-token bill. • Runs beside our whole agent city on one box with ~130 GB to spare. • Review test: caught 6 of 6 planted P1 defects, zero false positives, same score as the frontier model we pay for. • CRM test: 11 of 11 required records extracted, zero wrong writes, 45 minutes, first local model to clear the bar. • Serving a 131k-token window today; the model itself supports 1,048,576. Widened to 4 concurrent slots and still have 50gb+ of excess ram.
granted my cto still isn't moving all of our inference to glm5.3 but we've identified 40%+ that is currently handled by fable that we're routing locally instead and will do concurrent requests to verify/compare responses for a while.
Are you using the right configuration for your own CPU?
On a Laptop with 32 GB RAM and Iris Xe integrated graphic card, I get between 11-18 Tokens/Second with Qwen 3.8 27B and llama.cpp with sysl Intel optimisations. Same results with the vulkan back end, although sometimes it ends in weird segmentation faults due to the memory consumption.
I have an M4 pro (48 GB ram) and I run Gemma 4 26b a4b at 52 tok/s and Qwen 3.5b a3b at 72 tok/s. Both 4bit quantized. These are enough for my needs and the performance is more than good enough. I'm not running the MLX version of the Gemma model, if I did the inference speed would likely be a bit better. I wouldn't use them for coding features though.
> enough for my needs
Which are...?
Some examples (keep in mind this is all indefinitely free for me, no burning quota away):
1. Getting information (such as information about hardware unfamiliar to me) when not connected to the internet, which happens occasionally in my case.
2. Continuing to learn Rust by way of toy examples, puzzles, and comparing aspects of various solutions, for example from LeetCode.
3. Reformatting data, for example from a PDF to a markdown table, or converting receipt images to text.
4. Simple translation/explanation (e.g. I'm teaching my wife one of the languages I speak but sometimes may not know/have the words to explain the full nuance of a translated word).
5. Summarization. One of the webnovels I'm reading has some very boring parts I don't want to slog through, in those cases I simply make the LLM summarize that part and move on.
Etc., you get the idea. It's not unusable for coding, but it would make many mistakes when making a whole feature and the context lengths are limited to around 30k-40k tokens by my RAM. I could give it access to the web but I simply use an online model when I need that sort of thing, again partly due to the context limit.
Edit: The MLX version of Gemma 4 26b a4b does about 62 tok/s.
> I'm able to load a bunch of different models on my little mini-PC with 16GB RAM, but the performance is terrible
With all due respect, I'm not clear why you are so surprised ?
By your own admission its a little mini-PC with 16GB RAM, I'm not sure what miracles you were expecting ?
Its a bit like complaining Rasperry Pi performance is terrible when trying to compile the Linux kernel.
I can't imagine using CPU... Oh I did twice.
If you are work from home and do dishes between prompts you can get a gpt3-like result.
I found it useful when I was... Well I didn't find it useful. But an Nvidia 3060 let me ask unethical questions pretty fast.
With which model. I have a 3060 with a bunch of system ram
It’s not. Do it as a hobby or for privacy but for performance just use a frontier model api. You’re paying less than cost for something that would take tens of thousands to set up locally.
That's not even remotely close to being true, even once you account for capex. You have to look at the actual usage, look at the token limits. Even if you're paying Anthropic $200k/month for scale-tier, you're going to blow through your token limits trying to run max output 24/7. Three users running Opus 4.8 at max non-stop will probably clean your monthly allowance from daddy Dario in less than a week.
With an 8x MI355x cluster at full tilt and including cooling, your power draw runs ~17kW. That's what it looks like when it's running full tilt. To be fair, hey that's pretty expensive. It does mean 8 multi-trillion parameter models unquantized running 24/7 without pause. And you get the full month like that, your monthly token limit is the time in a month. That cluster, the electrical upgrade, the cooling setup, and the electricity to run it all costs less in 2 months than your maximum affordance from Anthropic does in the same time period. Two billing cycles, and realistically it's more like two weeks. In 4 quarters you've wasted over a million. Like, what are we talking about here?
Now if you aren't using AI all that much, which is perfectly valid, and especially if you aren't using it at its absolute maximum, the story changes. Because even though at that point you're not paying nearly as much in electricity to run the cluster anymore, you still have the $300k+ capex to get the setup in the first place. But if we're not redlining it non-stop, then we're not really talking about performance anymore, are we? If your org never comes close to hitting token limits, it's probably because AI is rather marginal for you. Which again, is perfectly valid. I don't even use AI professionally.
Fact of the matter is, if your corp can justify the capex for a cluster and makes heavy use of AI, you are literally burning money by not having one in your building. The numbers are painfully obvious. Even deepseek isn't as cheap. This is before we get into things like LoRAs, custom inference pipelines, etc. which you know are kind of important if you actually care about model performance.
Here’s an experiment: purchase an anthropic pro max subscription for $200/m. Now go buy the hardware to run DeepSeek’s equivalent. In a year, who spent more?
In normal times in which hardware used to depreciate (lately that's not the case and HW even appreciates, but let's not get distracted), if you calculate only with depreciation costs, plus the fact that when you have such a setup, it'd take many 200$ subs to cover your lack of limits in the other, I think it'd not be a clear victory for any side.
If you just ask "who spent more in the first year" (100% depreciation) then even with 5-6 max accounts, buying HW will be a couple of times more expensive. But when does it make sense to ask that question?
Maybe the SotA models will need better hardware so your investment will not be useful after a year or you'd need very expensive upgrades? But then (as in Fable case) subscribers need to spend more too.
It’s not so clear after 5 years that you’ll come out ahead. You’ll have spent $20k. The apple computer owner will probably be running local models that are better than today’s frontier on the same hardware.
Idk where you live, but where I am running the M5 Ultra Mac Studio at max rated power 24/7 for a month costs C$42.
The considerations against Apple hardware are 1) hardware advancements 2) early access to the best models. But it’s really not that clear.
(The other guy who thought hosted models on openrouter are cheap has spent $100k in 5 years.)
Depends on what you plan to do.
You don't need frontier models to summarise or create an email.
That's not apples to apples on almost any dimension.
> With an 8x MI355x cluster at full tilt and including cooling, your power draw runs ~17kW. That's what it looks like when it's running full tilt. To be fair, hey that's pretty expensive.
Pretty expensive is an understatement. You couldn’t buy one of these if you wanted to right now. If you could it would be multiple hundreds of thousands of dollars.
> It does mean 8 multi-trillion parameter models unquantized running 24/7 without pause
You can’t even run one unquantized multi-trillion parameter (>=2T) model on 8 x MI355x with enough context for concurrent users. I don’t know how you think it’s going to run 8 of them at the same time. Did you mean 8 concurrent sessions?
Your math is way off across this post. If replacing an Anthropic subscription for a whole company was as easy as buying a box for the office and then breaking even in 2 months, it wouldn’t be some little secret that we only discover in a comment online.
>You couldn’t buy one of these if you wanted to right now.
You can: https://www.exxactcorp.com/Exxact-TS4-149591758-E149591758 . You can get thousands of tps of GLM 5.3 output out of this thing, which grades around Opus 4.8. Payoff is around 1 year vs. spot prices on these GPUs, including power.
> You can: https://www.exxactcorp.com/Exxact-TS4-149591758-E149591758 .
No, you can get a quote for possibly being allocated one in the distant future.
The backlog for these is huge. You cannot buy one any time soon.
Ah gotcha. Have you tried to order something like this in the past?
I have quoted large nodes from this supplier and have lots of^W^W GPUs from them for personal use. Current lead time is more than 30 months.
They're a good provider but you have to be a big shot buying NVL72s before you're getting anything within your payback period.
Ah thanks for the solid info, too bad. I'd seen them come up as a pretty good price for 6000 RTX's in the past, which seem generally pretty available, good source for those?
Yeah, they're good source. But the price for those GPUs is 5 figs even with the nvidia startup program nowadays. Also, I went back and looked. Most of my GPUs are actually from Central Computers who were great, but Exxact is real too. So "lots of" was inaccurate.
Also, the lead time I quoted was for individual 8x nodes.
Ah yeah, one of mine is from Central. And yeah, crazy how much they've gone up. But I can see why, they scream.
I can't tell from the ad -- it says "supports" 8x MI350X GPUs, but does that mean "includes" 8x MI350X GPUs? For $300K I'd certainly hope so, but I'm assuming not.
A system with 4x RTX 6000s costs about $60K these days, and can (as you note) trade blows with Opus 4.8 if not Fable. In fact, it'll give you a better pelican than Fable 5.1, and in less time.
Ha fair, I'd definitely confirm with a salesperson before wiring them $300k. But most of the signs on the configurator seem to point to it including the GPUs? Not going to make 30k BTUs/hr of heat without the 8kw of GPUs.
> trade blows with Opus 4.8 if not Fable.
Okay I love the open models, but the hype is getting ridiculous. The models you can run on 4 X RTX6000 are not Fable level.
Baseline yeah. But part of the reason you run open models is how much nicer fine tuning them is. Granted, you probably don't want to try and make LoRAs on a 4x RTX6000 setup, but you could if you really wanted to and there are other ways to modify models. And yes, if you're good at it, you can turn a piddly mid-range model that's only good at benchmarks into a heavyweight clanker (for a specific domain).
Well, they are if you're into animating pelicans. :-P But yes, in the general case Opus is a better match.
And Opus is no slouch. I'm satisfied that GLM 5.3 is just as strong as Opus. Z.AI has promised/bragged that they will be at Fable 5.0 level by the end of the year or early next year, and I don't see any reason to doubt them.
> Pretty expensive is an understatement. [...] If you could it would be multiple hundreds of thousands of dollars.
Obviously, I quantified both the operating expense and the capital expense in my post. What I find curious is that you're quoting me talking about the operating expenditure, and changing the topic to be about the buy-in like these are interchangeable things. You don't think that this is a crucial and important distinction?
> You couldn’t buy one of these if you wanted to right now.
You could have spent all of 5 seconds of searching rather than just assuming[1]. You're not buying an Nvidia Superpod™.
> You can’t even run one unquantized multi-trillion parameter (>=2T) model on 8 x MI355x with enough context for concurrent users.
That's certainly fair a point. Although in the English language, especially in legal contexts, the multi- prefix is used inclusively for fractional values. That is it's strictly >1, not >=2. IE an 18 month contract is a multi-year contract, or a $1.6 million dollar asset is a "multi-million" dollar asset. But this is uninteresting semantics.
You are right, but it also doesn't matter. The gap is just that big. You can run 1 single user of Kimi K3 and still not even come remotely close to the $70k or so that a single Opus 4.8 user can burn over the course of a month on left on max. An honestly lowballed amount I know from anecdote. The per-token cost is just really expensive.
> Your math is way off across this post.
You made one technical point above, one that doesn't ever arrive at a relevant rebuttal to the substance of my post. But please, I'd love to hear you elaborate, especially because I didn't actually give much math at all.
If you want math though, here's the math. Let's say you are paying a ridiculous amount of money for electricity, a price nobody in the US pays -- $2 per kilowatt hour. That's about 5x the average rate in California, 4x as in Hawai'i. 17kW @ $2/kWh * ~8766 hours in a year puts that cluster's electrical costs at just shy of ~$298k annually assuming it takes no breaks. Let's make matters worse and round that up to $300k. It's also assuming you didn't invest in a solar hookup for your building, which I don't know why you haven't at this point, especially if you're installing a CDU for your new cluster. 12 months of Claude burning $70k a month is $840k. For a buy in of, you know what, let's call it $500k. Why not? It still doesn't matter. The operating cost is so much lower it's paid for itself plus an additional $40k in the first year. Even at a ridiculous penalty in electricity that nobody pays, even overinflating the amount of money you'd pay for the cluster and the infrastructure to get it set up, it's not even remotely close for a single user where the gap is smaller (IE, you're not wasting "a million dollars" in a year by maxing out the $200k scaling limit every month)
You can of course trot out the point that oh, in 12 months this setup will be extremely outdated! It doesn't matter. If the work it was doing today was useful, it will be useful next year too. And with the rapidly encroaching diminishing returns from parameter scaling, you're probably going to be just fine for a while. Maybe grab a quantized version of a newer Chinese model at the end, before grabbing a newer generation of AMD node. Those MI400s are looking pretty sweet after all.
> If replacing an Anthropic subscription for a whole company was as easy as buying a box for the office and then breaking even in 2 months
If you're locked in, then you're locked in. But don't pretend like you're saving money. You're not.
> it wouldn’t be some little secret that we only discover in a comment online.
Why does this have you so nasty and defensive? It's not a "little secret" that running your own infrastructure is cheaper. Of course it is. You know what else is cheaper? Owning your own office building out in the sticks, rather than leasing part of one in the city. Not everybody can make that work, there are no free lunches after all.
History repeats, these same exact lines were rolled out ad nauseum during the cloud craze. Datacenters are businesses, not charities. Frontier companies rent quite a fair amount of their infrastructure. Even if they resold that compute below cost (they don't), there's a pretty steep cliff before the economics start to look attractive.
[1] - https://www.avadirect.com/GIGABYTE-G893-ZX1-AAX4-Dual-AMD-EP...
> You could have spent all of 5 seconds of searching rather than just assuming[1].
I guarantee this will not ship to you any time soon.
The current lead time on these GPUs in measured in years. If you didn't place an order for this a long time ago, it's not coming this year.
Being able to add it to an online configurator does not mean anything right now.
> 12 months of Claude burning $70k a month is $840k
Your math is completely useless with these arbitrary numbers pulled out of the air.
If you want to begin calculating payback period you'd need to look at token costs, cost per task, utilization rates, and so on.
> The operating cost is so much lower it's paid for itself plus an additional $40k in the first year.
You went from paying back in a couple months to paying back in a year but you still haven't even talked about tokens or concurrency.
You're also neglecting the fact that hosted tokens are going down in price at a rapid rate. If someone was paying $70K per month in tokens for Opus this month, that same level of compute is going to be much cheaper 12 months from now.
> Why does this have you so nasty and defensive?
Not nasty or defensive, just tired of these armchair claims that it's easy to go out and buy an 8 X MI355X box from people who obviously have no idea what the hardware lead time is like right now, or who haven't considered the actual math on token costs and payback times. You're still making a lot of claims without a single discussion of cost per task or token.
What're you using that monster for?
It does make me wonder how the hosted stuff is so cheap. For pretty much everything else, hosted/rented is more expensive but offers better convenience and flexibility. But for AI, even if you consider the total lifetime cost and are utilizing it heavily. You never break even by buying.
They're not cheap at all. I did one xhigh Qwen 3.8 27B agentic coding task last week via OpenRouter and it cost me like $10.
99% of the cost was in input tokens, I only used like 100k ish output tokens. It was a one shot task asking the agent to implement proxy injection to Guice. It did a pretty amazing job.
If you were to use hosted LLMs for a lot of agentic coding, a maxed out M5 Ultra Mac Studio would pay for itself in under a year.
Qwen is weirdly expensive. Deepseek v4 flash is dirt cheap. You'd need at least 128gb of ram to run this model and in my experience, a days work with it costs around 80 cents.
So I ran the math, assuming the agent takes 75 turns per 200k context, with deepseek v4 flash it costs around $2.57 to reach 1M context in 375 turns. Cached input costs scale quadratically with # of agent turns.
Considering that I hit the 1M compaction multiple times per day with codex, it would definitely cost at least $5-8/day to use deepseek how I normally use codex.
It's a time sharing agreement, just like old-school mainframes and such. You're not getting a full machine to yourself, but a few cycles at a time.
There are economies of scale but there’s also a data center bubble (probably) so there might be some selling dollars for fifty cents going on.
I watched someone at a fortune 20 company get embarrassed for buying a Mac to run a 70B model in 2025.
He was a lead engineer, so after he announced it wasn't going to work, everyone pretended it never happened. But we all knew.
Sounds like a really rude workplace. Who cares if he wants to try running things locally?
These are estimates: https://tokenstead.ai/find/results?hardware_id=23&use_case=c...
I honestly wouldn’t bother with local models right now unless I either had a 5090 and was happy with running Qwen 3.8 27B, or a pair of DGX Sparks running DSv4 flash, or better, 2x6000 RTX Blackwells. Those are the kinds of rigs that the local model enthusiasts are running. With the GPU setups, you’re looking at generally >100tps generation in single stream, and >10k tps of prefill, so it’s snappier than Claude code, which somewhat makes up for it being dumber.
That said, it is really cool to be able to run an LLM on eg a Mac laptop. Just not a better experience on almost any metric for interactive use than eg Claude Code, beside privacy and guardrails.
>I honestly wouldn’t bother with local models right now unless I either had a 5090 and was happy with running Qwen 3.8 27B
How's the actual performance of Qwen 3.8 27B? On deepswe it supposedly performs slightly worse than gpt 5.6 luna high[1], but I can't help but think they've been benchmaxxed.
[1] https://deepswe.datacurve.ai/, https://unsloth.ai/docs/models/qwen3.8#benchmarks
Not sure, I haven't run it, I've just been running DS V4 Flash non-stop since it came out, and that's replaced a lot of my Claude Code usage. People seem very impressed, though, it seems like it trades vram/world knowledge for extra thinking time, which I think is a good trade for local. tbf, I've heard luna's not great at coding. Fast and good for things like classifiers, summarization, though.
A friend and I were actually discussing today how benches show Luna Max at about par on coding with Sol Medium, but how it's nowhere near in reality. We were speculating that maybe it's because a lot of benches are best-of-n, and should probably be worst-of-n, because variance in performance is killer with large coding projects. Consistency is what lets you actually build on this stuff.
Keep in mind these downloadable models use 3-10x the amount of tokens as well. You really can’t beat a couple $20 subscriptions.
https://quesma.com/benchmarks/babaisbench/
Looks like GLM 5.2 is coming in at <2x the tokens of Opus 4.8 (and 1/10 the cost)?
Great showing from Sol, though.
But also, it's Baba Is You :-D