Unless you really want privacy or the fuzzy feeling of owning your own, it’s cheaper, more convenient and has much faster tok/s if you pay a hyper scaler.
That said, I do like the direction we are heading and look forward to seeing what host your own hardware we get in 2 years.
At a theoretical 6 tok/s, 86400 seconds in a day, approx 500,000 tokens of GLM5.2 output for 2 bucks a day seems like a pretty good bargain to me. Of course not counting the one time cost of the hardware to run it. But I see people dropping $4000-5000 on all kinds of much less useful stuff.
Additionally in a place where people use electric baseboard heating or electric in floor radiant heating, or really any other heating element based system in winter that's less efficient than a heat pump, additional electrical from a computing load is basically "free" since you would be spending that same money otherwise to heat your house. If a computer with 512GB of RAM is dumping the waste heat into your room, it accomplishes a portion of the same thing as a baseboard.
Not to mention there is a whole other less measurable benefit of having a locally hosted model that can't be turned off or arbitrarily restricted by a service provider, and where all of your queries and context cache aren't subject to surveillance by any third party.
On Openrouter, the cheapest GLM 5.2 provider costs $3/MTok (at 44 tps). Assuming most use is output tokens, that's still the equivalent of 450k token/day, so we're in the same ball park, but without the capex for 2 3090's and the machine.
Self hosted only makes economic sense if your priority is being in control / avoiding surveillance.
Running a system that will be 600W under max CPU usage on all cores and RAM and a few 3090-class GPUs, that same system might be only 90W or around there when idle at 0.00 unix load.
If we say: (600 * 24 * 31)/1000 = 446kWh in a month at full load 24 hours a day
But it could be less, such as: (90 * 12 * 31)/1000 = 33.48 kWh of idle time in a month, and 223kWh of "full load" 600W time in a month, if it's at full load only 12 hours a day.
If you're the only user accessing it and you only "use" it 12 hours a day, that cumulative yearly dollar figure would be almost halved. Or even less if a person is using it in bursts and intermittently throughout an 8 hour workday.
You can’t do that with 6 tps, though.
No, you would pay usage based rates with API, in this case. I have exactly one fixed monthly rate for the 6 AI models I have tokens available for.
It isn't 100% efficient. Even the best PSUs aren't.
There is no "ubiquitous" geothermal where there also high power usage. Data centers have to go where power is, not can be.
[1] https://en.wikipedia.org/wiki/List_of_geothermal_power_stati...
I think the main reason not to run locally is to get the full models instead of quantized versions.
I agree and I prefer on-prem where possible. The Apple Mac Studios have been great for that although I don't have enough of them to run GLM-5.2 without heavy quantization. I'm also waiting for the Apple next product refresh which I hope will enable me to do more with less.
Meanwhile there are hosted privacy-conscious options out there. Two names to look at are Tinfoil[1] and Privatemode (from Edgeless Systems)[2].
Tinfoil[1] is, sadly, US-based. EU-sovereignty-option is on their long-term radar. But they do have GLM-5.2 today.
Privatemode[2] is a German company (Edgeless Systems) with EU-based servers. But sadly no GLM-5.2 today, it is on their mid-long term radar though.
Both Tinfoil and Privatemode operate on the same concept of the LLM operating in a secure enclave and you have end-to-end attestation and encryption.
Tinfoil have not been independently audited, it is somewhere on their long-term radar.
Privatemode have been thoroughly independently audited with documentation available on request.
Both of them are API-tokens-only. So if you're currently one of those people throwing $200 a month down the pan at Anthropic/OpenAI for a so-called-alleged 'unlimited' plan, then neither Tinfoil or Privatemode will be the place for you.
I have this feeling that it'll be very expensive and still scarce. Normally I wouldn't say this about Apple, because their pricing is part of their brand, but this time the demand (both by data-centers and prosumers) is the force majeure.
I know people usually say that about Apple, but to be fair to them on this occasion they have not hiked up their prices yet because they are clearly at present still under some old deals that they did a good job negotiating.
However, of course, at some point Apple will run out of both inventory and old-pricing manufacturing capacity. Yes, I am fully expecting some sort of price-hike like has been seen everywhere else. I am not naïve.
When that time comes it will remain a financial calculation, Apple boxes on one side versus hosted-option-costs on another, in relation to my specific use-cases.
Ultimately I still blame the chip-hoarding hyperscalers though. :)
Or cloud LLM might just refuse to sell to you because it dont like your passport.
Like buying a new car today and taking on gas, parking, etc, expenses in case the bus route you’re using goes away at some point in the future. It’s not an economic decision, it’s a desire to have the new car dressed up in what-ifs.
Any more tortured metaphors in store for us?
As soon as VRAM prices drop to sanity I'm going to load up and I could care less about the power draw.
Some parts of the future are absolutely great.
Anyway, I think GLM 5.2 in many ways is not as interesting as DeepSeek V4 series, which uses an even more advanced attention mechanism and can save a lot of memory capacity for KV cache, especially at larger contexts. Which in turn opens up wide batching especially on consumer platforms. GLM doesn't have that, in some ways it feels broadly similar to Kimi 2.6 wrt. the underlying performance architecture. Both are a bit too heavy to run reasonably at full quality on ordinary hardware.
It also has an input image modality, which is a game changer. The cheap Sinofrontier models have generally been lacking in this regard.
Basically, Chinese competition is fierce - DeepSeek set the pricing tier, and the question for each lab now is how to justify charging a little more.
MiMo-2.5-Pro has gone with UltraSoeed, pumping out 1000t/s for a 3X price hike.
GLM has gone with 5.2, hitting Opus levels of reasoning at a fraction of the cost.
DeepSeek will probably keep their pricing model and just keep getting better and better.
Qwen-3.7 is the dark horse. Some rumours are Alibaba is simply making these models because they need them internally.
The real question is why this level of innovation and competition isn’t happening in America or Europe. In particular I see no reason Europe doesn’t have a lab competing on these terms.
Europe can provide none of this. They will never be at the frontier of AI tech, for the same reason they were never at the frontier of any tech.
I say this as a software engineer from Europe.
Qualify it to software, rather than all tech, if you will.
How about addressing this false dichotomy with the likelihood that someone who is new or interested in a tech isn't willing to drop thousands of dollars on used hardware for a whim or learning exercise.
32 CPU Epyc (Epyc is required for faster memory access) + 32 GB VRAM + 512 GB RAM is stupid expensive nowadays, and in best case, it will just downgrade to "very" expensive at some point in the future.
This makes sense only if 1. one is paranoid about privacy or 2. they have money to smoke or 3. they need to workaround cloud model restrictions, AND they have to do it routinely (because if not, a oneshot cloud bare metal setup is way cheaper, faster, and allows more powerful models, due to VRAM offering).
I did spend stupid money as well and yet, the system is 2x slower than cloud providers for comparable performance on vision tasks (I still have to test coding). Oh, and it's hot as hell.
Can you put up with that? As seems very slow. I aim for 40t/s on a laptop and choose models that deliver that speed over larger slower ones
Incase it's not clear, you will be generating 10,000,000 a second. Good luck verifying it. Token generation is not the bottleneck for creative work. If you are doing a predictable work and have a good workflow and massive dataset to process, then speed of token matters. If you are performing creative work like coding, it doesn't.
Apart of running local models I use this rig as my main remote development platform. All Claude Code sessions are running there in tmux now. And my fingers can't be happier not having to deal with constantly hot laptop. Not to mention that Claude Code is such a battery hog.
[0] https://medium.com/@rathko/i-built-an-epyc-64-core-512gb-ram...
Or maybe the model itself only runs at gpus, and the cpu memory only store the weights for experts not corrently activated? If so, then what's the 32 or 64 cpu cores for?
I'm a big fan of fully utilizing one's hardware and it's kinda sad that it's not the norm to run things on either gpu, cpu or both, dynamically choosing at runtime, for everyday software
https://github.com/noonghunna/club-3090/blob/master/docs/DUA...
Cloud offerings are 80-200tk/sec versus single digit tk/sec.
That said, I'm also surprised it runs at all locally. I do think it'd be painfully slow for anything interactive so you're relying on another model for a comprehensive design or you're hoping a one-shot with somewhat degraded quality turns out correctly.
Local inference needs really hard a 1.2 / 1.5 T/s memory bandwidth system with 512GB and 2/3 times the GPU compute of Mac Studio M3 Ultra, at an affordable 10/15k price point. A variant with 1TB memory would also be welcomed at 20k price point.
It will be interesting to see how this model does under a SSD streaming scenario, the lower sparsity should ideally be favorable.
> Local inference needs really hard a 1.2 / 1.5 T/s memory bandwidth system with 512GB and 2/3 times the GPU compute of Mac Studio M3 Ultra, at an affordable 10/15k price point. A variant with 1TB memory would also be welcomed at 20k price point.
Are these realistic specs at present? Not that clear to me, 1.5 T/s seems really high.
https://unsloth.ai/docs/models/glm-5.2#usage-guide
In a prior thread, someone said it would take $500k in hardware:
NVFP4 at reasonable speeds (~120 tok/s) and concurrency is possible at a $80/90k figure with today's prices, maybe even less. That buys you 6 RTX 6000 PRO Blackwells, a decent CPU and motherboard, power supply. 576gb of VRAM.
You could do it for under $50k if you're OK with 40 tok/s decode, ~1200 tok/s prefill.
Official price 85k...
The problem is the backplane I have not managed to find a single baseboard, and getting a random baseboard to work with random modules is probably a crap shoot.
In late 2027 or early 2028, Nvidia will release Vera Rubin DGX Spark, likely with double or better the performance of current Blackwell, though unclear if memory capacity will go up much from current 128GB. Two to four of those will run models like this decently.
In 2028 we should expect Vera Rubin RTX discrete lineup, including the replacement to the RTX PRO 6000. Likely memory spec will be minimum 128GB. Good chance of up to 200GB. Two to four of those will run NVFP4 models in this class very well.
I’ve found that I need to go a couple steps past whatever quantizations are good enough in the KL-divergence testing to get good performance in real tasks with long context. So when Q4 is claimed to be lossless I end up with Q5 or Q6 for actual long-context tasks.
I feel like part of the reason for the relative stagnation in hardware over the last twenty years was simply the lack of use cases to justify hardware refreshes by businesses.
Most of the money and energy went to mobile for the last fifteen years.
Affordable local inference might be the gravy train the server, desktop, and laptop manufacturers need to get back in gear.
Business hardware got beefier because businesses demanded more data (or more specifically: the industry told businesses they needed more data), with no idea of what to actually do with it once they got it. To get all that data, bandwidth needed to be increased, with more iops to read/write it, more storage to keep it, and more memory and cpu to process it. But 99% of the data is junk. Companies have "data lakes" so big they need to come up with excuses to use the data, or risk somebody pointing out that they're spending a fortune hoarding bits.
Consumer hardware hasn't had a new use case since like 2012. Faster wifi for broadband & local file transfers, and higher-resolution video, are the only reasons one needed newer hardware. We actually got a resolution so high it makes no perceivable difference. And yeah we got faster CPUs and memory, but as soon as we did it got all eaten up by the most inefficient, wasteful software conceivable. Same use cases as 13 years ago, just more expensive, harder to use, and buggier. We should've gotten a new CPU architecture that was faster and more energy efficient. Finally it was delivered, but with a moat around the golden Apple.
Here we are two and a half decades into the Internet era, and my damn bluetooth earbuds and webcam microphone don't work half the time that I open a video conferencing app. Hardware can stay exactly like it is for the next few decades and I'd be happy. I just want software that works, and doesn't get continuously slower, forcing me to buy bigger hardware; or more draconian, locking me out of being able to use it how I want.
No, we're running into limits of moore's law, and it's showing in prices for new nodes, where they're getting denser but not cheaper.
So we hit limits on clock speed in the early 2000s (ex - the 4ghz wall) but it also turned out that mobile as the driver for sales meant no one really cared much about clock speed compared to performance/watt.
Clock speed mattered, but only relative to how many watts it took to get it (and above 4ghz... too many watts).
But we've seen a 15x improvement over the last 20 years. Performance/Watt is WAY up.
My guess is that LLMs are going to drive another "improvement cycle" in areas that we didn't care much about before.
I've built about 10 personal desktop machines (1 every ~4 years) and I can honestly say that I didn't care much about memory bandwidth prior to 2021.
In the same way that I didn't care much about how many watts my pentium 4 was using in 2005.
But now... now I care a lot about memory bandwidth. I care about memory speeds and total system ram in a manner I really, really didn't before.
So I think we're going to see a big shift to machines built on unified ram with a crazy focus on squeezing memory bandwidth and total ram capacity as far as we can.
My bet is that we'll get a similar 10-15x improvement by 2040 in unified system ram designs.
I fully expect to see 2tb unified ram desktops and 200gb unified ram phones be relatively common on a 20 year timeline, assuming we see similar levels of geopolitical stability (ex - world war 3 throws a wrench into things).
In the old days, Microsoft Entertainment Pack games were somewhat visibly taxing on some lower end systems.
The page advertises the 8-bit quant as taking ~800GB, which seems like it would require at least 3 consumer motherboards fully stacked w/ 4x64GB cards each.
Maybe “locally” has slowly come to imply “…on your homelab”?
I was lucky to buy a lot of RAM before prices skyrocketed. I knew I wanted to play with this stuff, so I spent what felt like a lot of money at the time to buy 8x96GB DDR5-6400 RDIMMs. Now the same RAM costs at least 6x more.
It wasn’t that absurdly expensive for a hobby, I bought 64GB DDR4 ECC sticks between $70-$100 on eBay before everything took off. Now everyone is in here debating if open source is 1 month or 3 months behind SOTA. The future is obviously local.
Speed-wise, I don't have numbers, but it feels subjectively faster than Opus in Claude Code. YMMV.
Once you go above "a used 3090 at a decentish price", then I strongly recommend renting cloud GPUs or at least testing models using paid APIs. This allows testing your use case before spending piles of money.
Each token has to read all the active weights. I think that's around 40B parameters active. At a 4-bit quant that's 20GB. With 100GB/s (replace with whatever your bandwidth is) and you get 5 tokens per second.
A model like GLM-5.2 being available as GGUF, usable through llama.cpp/Ollama/vLLM/SGLang/LM Studio, and wrapped for local agent workflows changes the category. It stops being an impressive open model exists and starts becoming this is something a small team can actually put into its development stack.
For instance, company buys an RX6000 setup for say $15k total. They could use this for handling data heavy sifting that would otherwise be a lot of Claude tokens.
It doesn't need to be as good as frontier-best. Just good enough.
I could see a business of people packaging this and handing it to companies who want Help Desk bots without any extra setup.
Considering they might be spending thousands per month on API costs already, dropping 15K to save on one process might not be bad. On the other hand, also an opportunity to sell GLM 5.2 inference at near cost to other companies for less than whatever Claude costs. In theory it costs anywhere from $0.51 to less than $2 an hour to run it and use it 24/7 that's still wildly cheaper than calling Opus which doesn't bill per hour, but per million tokens, drastically higher. Hell, you could probably bill at $5 per GPU hour and still be cheaper. Whether you're looking to self-host or sell hosting for it, it looks way cheaper regardless. I think most decent open models will continue to fit in at least 32GB of VRAM so a 6000 Pro GPU is more than enough. alternatively, even on a 5090 you can get a reasonable amount of inference for way less than paying for Opus, Qwen would be your friend there though.
On top of that, you will still be heavily quantized.
You can cluster these beasts too. Two and three (with two IP subnets) is fairly obvious. Four or more might need a switch depending on how much network latency affects things.
Apple seem to have forgotten about M series with gobs of RAM. I can't get the Apple shop to show more than 96GB of unified RAM and that costs a kidney.
If you are training and doing research it's great, if you want to cluster them it cant be beat, but if you just want local inference on a single box buy a mac or even a strix halo device.
I'm a Linux guy, but also don't always have alot of time. The Spark comes out of the box with a nice Linux distro that's pre-configured to be easy to setup and the guides and online resources make getting up and running trivial, for even some complex tasks. You would have to do a LOT of tinkering just to figure out some of the things the nvidia resources walk you through natively. They have guides for a ton of stuff that include the optimal settings so you don't have to figure it all out through trial and error.
Check out these "playbooks" for some examples. [0] There's a lot to be said for not having to piece all that together yourself.
https://build.nvidia.com/spark
I think between unboxing mine setting it up to run headless, and generating tokens was like 20 minutes total for me.
Only the M3 Ultra really beats it, and once you start scoping out the cost of a M3 Ultra with 128GB or 256GB, the DGX Spark doesn’t look bad after all.
I see ~274 GB/sec for the DGX Spark[1], versus 307 GB/sec for M5 Pro and 460 or 614 GB/sec for M5 Max[2]. One might call 90% "basically the same", but there are nominally two tiers above "Pro".
Yes, a MacBook Pro with 128 GB and M5 Max costs $5100 (14") or $5400 (16") versus currently $4700 for the DGX Spark, but the MBP includes keyboard, mouse, battery and portability. I believe its prefill is slower and you get 2 TB vs 4 TB SSD, but overall one gives up a lot to save 10% of the cost.
[1]- https://docs.nvidia.com/dgx/dgx-spark/hardware.html [2]- https://support.apple.com/en-us/126319
Apple could actually be a good deal and you folks would still make up something to not justify it. In a way, it’s amazing what Apple has accomplished- Baseless negatively-tainted perception in certain influential tech circles.
(To be fair, they’re kind of earning it. I’m glad Tim “Sweet T” Cook is departing.)
Plus, my original comment got downvoted despite being factually-correct. Thanks, Reddit. Oh, wait…
The spark can fine tune models in 1/4 the time and excels at other compute tasks in ways that Mac never can. Plus the high bandwidth ConnectX-7 ports would be like $1700 to buy on a card just for the network adapters... But for generating tokens, it just plain loses.
(Still potentially very useful! But not magically ultra fast.)
- new AI desktops with GB10s. They are relatively cheap and you can cluster them and load 1TB of VRAM
- Nvidia, amd, intel, Cerebras etc pushing new hardware
- oss models getting crazy good, like glm 5.2
- flash models getting very good like deepseek V4 flash
- quantizations
- harnesses being able to use different models (big for difficult stuff, small for grunt work)
So hopefully soon for the ones who want to break free from APIs, we will be able to host at home a cluster of AI desktops at a reasonable price with Opus-level capabilities, can't wait!!
LLM companies are valued as if they're going to have some enduring monopoly that they can extract money from... GLM-5.2 and similar models make that valuation very very questionable.
No disagreement there, but it could easily last another 3 to 5 years which is a long time in tech terms.
Don't underestimate the markets ability to remain irrational
If making RAM and SSDs is now cause for a 10 figure valuation, after enough time somebody will dive in.
Also irrational parts of this market (would love to hear your thoughts):
- the purchase of hardware that isn't power efficient or gives an ROI for ML/AI use cases by companies buying it, who would be priced out of using that hardware over time
- many people and companies are buying the hardware due to hype and scarcity/FOMO reasons over rational reasons
Alibaba released Qwen 3.6 "tiny" models not that long ago, they punch way above their weight(s)
True, Qwen3.6-27B is amazing for it's size. However, it seems likely that we're not going to see anymore of these smaller models from Alibaba/Qwen since several key players exited that organization a few months back.
For companies wanting LLMs in their products in general, I have to think going the local llm route is even more tempting. Somewhat dumb models are more than good enough for a lot of the things people are integrating LLMs into their products.
> As long as that is allowed to happen
It won't be. Only we can provide that, and only for ourselves.
Having said that, I don’t think it’s all that tempting for companies at all, considering this whole market is developing rapidly and it’s nearly impossible to predict where we’ll be at in a year or two.
It's not like you'd lose capabilities, if anything this solution just gets better with time.
I think you’re better off renting GPU instances and running all the software on those. It’ll be cheaper than Anthropic and OpenRouter but slightly more expensive than electricity and depreciation of hardware.
The number of parameters needed for these open weight models has mostly stabilized so the actual memory requirements aren't likely to change all that much.
TPS = active weights in GB / your memory bandwidth.
That’s it for decode. That’s all.
You need something like 6x RTX Pro 6000 at $11800 each plus a nice server (add $10000) = $80800 and then quite a bit of electricity.
For who pays for it, obviously the employer would.
For "how did I arrive at this number" Ballpark estimate from what I know about part cost. Most of that money will go towards AI cards about $5k for the mb, cpu, power supply, etc. $45k would be for as much ram and as big/expensive nVidia cards as you can get your hands on. The B300 has 288GB of VRAM in it. Probably what you'd be after.
think Apple M6 or M7 with a currently unforeseen denser memory style, 256gb RAM
a couple inference or cache improvements on the algorithmic side, using less ram for context windows and doubling token speed again
denser open source models, packing more experts for smaller active layers
it'll still be expensive but like $8,000 - $13,000 instead of $450,000 worth of B200s
I’m sorry, but I just can’t imagine us running smaller models than we are using right now in 5-10 years from now.
If a model needs 2x more memory, but serves the same number of customers, the cost is going to go up per customer to cover the increased hardware and power costs. Companies are starting to implement AI limits to keep costs under control.
Anthropic and OpenAI are rumored to be considering cutting inference prices trying to retain customers as LLMs commoditize and race to the bottom. It reminds me of the Chinese bike wars where bike-share companies were losing massive amounts of money, but kept running sales and lowering prices in an attempt to compete and drive out their competitors. The end of that story was a bunch of major bankruptcies and giant bike graveyards.
Nvidia's hard pivot to "in the near future, everyone will run their AI at home" seems to indicate that they also see the market shifting. We've already had AI ingest everything out there. The real challenge becomes how to better optimize their algorithm to get more useful data in less space.
a lot of innovation occurring
I might build one for fun, but it's not going to change the economics alone. Still exciting it's possible.
But if you already have agent loops dialed in (For example I have one that uses a browser testing framework), it wouldn’t really affect me at all if it crunched away at 7 tokens per second all night long.
Sure, it cannot replace SOTA models for agentic coding, except for small, well-scoped refactorings. But even a model like ministral-3:8b or qwen3.5:9b is a boon for so many smaller use cases!
(I only ask Opus every 5 to 10 requests, when my local Qwen fails or when I encounter a situation that is too world-knowledge specific to be worth asking, but that way you can live easily with Claude's cheapest plan without ever facing usage limit).
for $4k, you can get 20 months of claude max 200. i'd take claude over the hardware.
anthropic will have something to worry about when you can run a local model on your macbook that can code. but i think we're quite a ways off from that.
How so? Model capability at a fixed hardware level has been consistently (and rapidly) increasing. You might or might not be able to run state of the art 2 (or 4 or whatever) years from now but you can reasonably expect the hardware to last upwards of a decade with model performance consistently improving over that time frame.
You can get a tolerable (at least by some metrics) experience using 10 year old hardware today.
And never underestimate the potential for enshittification. Your local rig will only deliver better performance over time as more and more tweaks come out. With cloud services expect the opposite to happen as subsidies run out. It's entirely possible that they will intersect on a bang per buck basis within two years.
Of course its gonna lose value faster if something magical happen with hardware manufacturing, but you'll likely get 25% back at least.
On other side you cant really predict how valuable claude max gonna be in a year because Anthropic can further enshittify it.
Kinda shows they have a headstart rather than a magic moat
I'm super grateful to the open labs (who, importantly, do not have the word 'Open' in their name), all the more so to the likes of Ai2.
There is no magic moat indeed. It is math, engineering and of course copious amounts of data (and the political maneuvering required to secure it, e.g. how most everyone has trained on Anna's Archive by this point).
Do the runes make it smarter or just run faster (or both)?
There should be more native 4bit, 1.25bit and likewise models. Those actually work great while making them smaller in comparison. But I guess there is some reason for them being pretty niche.
First, it's not really "1 bit", actually much closer to 2-bit. IQ1_M is actually 1.75bit and IQ2_XXS is 2.06bit This is from the ./llama-quantize --help with most of the quant types and their size in bpw: https://pastebin.com/bCUqGfeE
And to elaborate on the "dynamic" aspect inconito said in the other comment, if you click on one of the .gguf files in huggingface:
https://huggingface.co/unsloth/GLM-5.2-GGUF/blob/main/UD-IQ1...
There are a lot of Q5_K, Q6_K, etc tensors. Only the routed experts (ffn_gate_exps.weight, ffn_up_exps.weight, ffn_down_exps.weight) are heavily quantized, and it looks like the down_proj is actually iq3_xxs for this model.
Offloading too many layers to CPU is not going to work at all. I have tried this many times and had to rm -rf on those heavy hf cache folders. Also I doubt 1-bit or 2-bit quants of GLM 5.2, running mostly outside of VRAM can beat Q8_0 of Qwen3.6-27B fully loaded in VRAM - on usefulness.
As I type this my local GLM5.2 is troubleshooting bugs that Qwen would not be able to handle.
But I don't know how usable GLM 5.2 is vs the Big 2.
...a bit of an odd question: how well do LLMs losslessly compress, as in for cold storage?
I definitely don't have the hardware to run this model at any kind of reasonable speed (and I don't want to use a super aggressive quantization that would kill performance). Even so, I think it would be cool to retain an offline copy, in case... I don't really know, a solar flare destroys the internet some day, or maybe a zombie apocalypse. It would just be cool to have.
But 1.5 TB is a bit too much! If it could be compressed down into something semi kind of reasonable, that would be fun!
1. Reduce the number of parameters
2. Reduce the resolution of each parameter (quantization)
For 1, changing the architecture is typically only possible by the labs producing the models, which is why each OSS model release tends to feature a small number of carefully chosen model sizes (for example, Gemma4 comes in e2B, e4B, 12B, 26Ba4B, and 31B sizes).
Generally, models with higher parameter counts have more world knowledge. For coding models, this shows up as a stronger command of uncommon libraries/languages. Very small models (<20B) also lack “smarts.”
Reducing the resolution of each parameter is easier which is why lots of practitioners have their own quantizations, but this makes it harder for a model to “think” fluently. Interacting with heavily quantized models feels like interacting with someone who didn’t get any sleep the night before.
Models that have higher-fidelity quantization take more RAM and have higher “smarts,” but don’t necessarily have more world knowledge. Models with aggressive quantization tend to be more likely to make rookie mistakes, emit malformed tool calls, get stuck in loops, or even exhibit signs of “neuroticism” / “distress” in their thinking tokens.
Parameter counts = world knowledge, quantization = “smarts.”
This is a soft rule of thumb, the difference isn’t very strong.
Here's the du output for GLM-5.2:
$ du -s -BG /cube/models/zai-org/GLM-5.2/
1099G /cube/models/zai-org/GLM-5.2/TBH this is like the near last ranking consideration in cost for being able to download and run this. Even though HDD and SSD prices have gone nuts as a result of the recent demand/shortage, it's not like 1.5TB of space costs a lot.
Even if you fed it into xzip with the most cpu intensive compression options and it didn't compress at all (eg: like trying to xzip an AV1 video, or whatever), it's still the cost of a single fast food hamburger meal in $/TB. The real concern is the RAM to run it.
But anyways, anecdotally, many 16-bit full precision GGUF files will compress to about 65% of original size with default xz options. I have a log here showing that's what IBM Granite 4.1 30b compressed to, which I'm keeping around but in lukewarm storage.
But I do like Unsloth Studio, quite a lot. It's nicely designed.
Only a select few have the hardware required to run this to begin with, and even then the forecasted performance makes me wonder if it’s worth it at all.
The key to a model this large is (1) Use it to plan, generate lots of plan and farm out to a smaller model. Then for very specific and complicated portions precisely prompt for what you need.
And yet Apple won't sell them to you anymore. And I'm not too confident it will be even possible to hand then 10k to get one again.
I am very excited for local LLMs I think we may have GPT 5.5-xhigh level of performance for under 2000 EUR
This should put more pressure on the frontier models to avoid sitting on any fancy stuff and lower token prices as a whole.
Nothing beats a local LLM disconnected from the cloud.
The other way this could go is that Z.ai could decide to release a smaller model targeted towards coding. They've done that before (GLM-4.7-Flash had 30B params). It would be great if they decided to release something in the 80B-100B param range. Something that size would easily run in a current Strix Halo system.
guess we'll be paying $200/month for a while
We are maybe 10 years off that.
RAM prices are going to continue to increase for the next 2 years at least.
Even putting that aside it's currently around 40-70,000 EUR to run this with a FP8 quantization (which you need to get close to maximum performance).
To actually get GPT 5.5-xhigh performance in the real world you need more headroom to support things like subagents (which will fill up your KV cache).
I like local models but realism is important. The sweet spot for the next 3 years will continue to be ~35B MoE models. They might match GPT 5.5-xhigh for chat-style problems but not for coding.
Also I m not sure where you are getting the under 2k value. I bought a Framework desktop 128GB last year and my setup was around 2.7k. The same setup now sells for around 4.7k.
Do not mix the benchmark results of GLM 5.2 FP16/FP8 with FP4 or FP2.
* FP4 will mean a accuracy loss of about 3%. Not noticeable but more chance for mistakes.
* FP2 ... what is what most people are able to run at home, for a "reasonable" price. Your looking at over 17% loss in accuracy.
At that point, your running at less then claude-sonnet-4.6, as the issues compound with accuracy losses. And reasonable priced is still in the ~ $5000 range (192GB + GPU 32GB active/kv cache system).
For that price your using a Codex / Claude Pro subscription for the next 4+ years with better models (by default), let alone with a FP2 GLM 5.2 version. And your looking at < 10 fps. A MacStudio with 512GB will net you 18 a 20fps+ with FP4, but ... i mean, those used to be $10.000.
Unfortunately the local hardware cost is a major issue for running large models like that.
Edit: Its funny whenever the issue of cost and what you need to give up vs the subscription services, there are always people who downvote in bad faith.
(it's not our first AI server, we already have experience deploying LLMs for our clients, so the numbers look solid)
And there is a lot of drama in those discussions. GLM 5.2 is a great model for corporations to run, but people only want to hear about running a 35B/27B or maybe a 120B model. And in that market, subscription services are simply way better value for money (take in account the privacy issues).
Everybody wants GPT 5.5/Opus 4.8 Max levels, on a budget that simply is not realistic. And GLM fit in that 4.8 medium/low level.
But then people do not want to be told that running a 750b model in Q2 or Q1 is just going to destroy the models accuracy. And that is still going to cost them 5k+ for that reduced model.
The whole local llm landscape from a consumer point of view, is just filled with odd people. lol.
Corporation really benefit from those models, because spending $90k on a server, is a deductible expense. And they are billed at token prices anyway from all the major providers. So its a even faster ROI on that hardware.
I am surprised that nobody figured out to make a business of selling leftover capacity from corporate llm installations, because there is easily 12h+ just wasted (unless its a large corp that has people in all timezones).
why is that? b/c the thing is waiting for the hoooman and idling? or some parallelizable interleaving steps?
I have no intuition yet how this works under the hood.
Waiting for the hooman (or tool calls) won't help either, of course.
In contrast, prompt prefill is more easily compute-bound, so there are interesting trade-offs for latency of decode vs prefill when the LLM utilization is high.
i think your answer was perfect not sure why you are being downvoted