Thank you for your work on DwarfStar! It is truly helping democratize access to frontier tech.

I applaud all you tinkerers for pushing on the state of the home-brewed art here. Like crypto, AI is drowned out by hucksters, very few people talk about developing resilience. Or the researchers who will push on open source models in efforts to cram them onto an electric toothbrush or tamagotchi. Bravo to you all.

Running that full load is at least 600 W, so in a day ~14 kWh. At $0.2 a kWH, that would be $2.80/day or $1k a year of op-ex in electricity.

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.

AIUI the llama.cpp implementation for this model is still quite half-baked due to missing the support for DSA sparse attention mechanism. This leads to running the model with a different mechanism that it has not been trained for, which has been shown to lead to lower quality and performance.

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.

"All it takes to run" might be fair if you paid $2400, but right now the total price is way closer to $10k (almost 5k for the RAM and 2k each for the GPUs). Today that is a lot of expensive hardware.

LOL, sure this works if one has a time machine or a LOT of money to burn.

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.

6 tokens per second?

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

I have pretty much almost this exact setup with 2x3090s and with slightly faster DDR4 512GB and 64 core Epyc! [0] I've been enjoying it a lot. Can't wait to give this model a try.

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...

I can work out max 90GB to the agents. Advise. :)

How can you combine CPU cores and multiple GPU? Are you running some layers in cpu, others in gpu #1, and others in gpu #2? What about the bandwidth and latency between them?

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

Very cool. So it's not just about GPU VRAM which I incorrectly thought. I though you'd need 512 GB GPU VRAM. I don't think it cost only 2400; 512GB ram would be more expensive though back in the day. But not mortgage-grade 200.000 which I estimated myself (which assumed running in 100% VRAM; overkill for a single user probably).

That's crazy good for $2400.

> 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.

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.

$500k is a vast overestimation. For massive concurrency at FP8 or even BF16 maybe.

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.

With 2 wouldn’t have good results. Ideal range for coding is at least Q8.

Crossing my fingers that this boom jumpstarts 90's like improvements in computing hardware.

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.

I’m kinda lost here… do y’all really have machines in your houses with hundreds of gigs of RAM?? Am I just behind the times?

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 have the RAM, but not the VRAM. What kind of speed/tps could you expect from a 3090 with 24GBs of RAM? I am somewhat tempted to pick a GPU with 24GBs of RAM.

Funny I casually asked Gemini and it said 500k for unquantized with decent throughput.

A nvidia spark thingie has 128GB unified RAM. They also have a dual port version of one of these things: https://www.nvidia.com/content/dam/en-zz/Solutions/networkin.... ie 2 x 100GB/s ports, they may even be 2 x 200GB/s. Once I've got my paws on one, I'll know more.

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.

My hope is on Intel Crescent Island with 480GB. I don't need 8x H200 performance (and cost), but I would like to run GLM 5.2 Q8.

I feel like "relatively" is doing a lot of work, there: at about $4k per GB10, that's $36k for a 1TB cluster. Cheap compared to equivalent H200's, but out of reach for home labs that aren't funded with OpenAI or Anthropic RSUs.

Hope you're right! Can't wait!

If we didn't have a RAM/GPU shortage right now they would be more nervous than they are. But as it is very few people are going to be able to afford a rig that can run this model effectively. That's probably not going to change for several more years yet. I think if the Z.ai folks decide to come out with a flash version of GLM-5.2 specialized for coding that came in about about 80B params, then the US frontier labs would probably be more worried. Overall, the Chinese AI companies have been showing the way to do the same amount with less (sometimes much less) and as that trend continues it's going to make the frontier labs worried - but even the Chinese AI companies are going to want to protect their moat by not releasing capable models that are significantly smaller than their current flagship models. AliBaba Qwen seems to be there now - it's gotten mighty quiet from them lately - their latest 395B model is just too large for most folks to run at home and they don't seem to be making any noises about releasing smaller ones this time around.

I don't think so. I could easily see a company deciding to host and run these models for their own development. If you have a dev team of about 10 people, a one time $50k investment in an LLM server has to be pretty tempting. Unlimited tokens, decent performance, upgrade options, and potential product integrations.

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.

You don't even need to run them locally for them to be a threat. Plenty of companies are looking at paying third party companies to host these models and they come in at fractions of the price of the frontier labs.

The RAM requirements are still pretty painful.

It's not economic to run them locally. It's amazing for privacy, and fun hobby. But you're either looking at super slow CPU builds with $10k in RAM, $90k worth of GPUs, or a really quantized model that doesn't compare in quality.

I might build one for fun, but it's not going to change the economics alone. Still exciting it's possible.

The hardware requirements to run this locally are still very high. Seems far enough off mainstream for those companies not to be too worried yet.

Honestly, Qwen3.6 is already what you need for the large majority of tasks.

(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).

locally on what hardware? something like the new dgx spark, ryzen halo, or mac studio will cost you ~ $4k plus whatever you pay for power. at the rate AI is currently progressing, i think you'd be optimistic to consider that as having a 2 year depreciation.

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.

Why do you think they are rushing to IPO!!

No. Unsloth has CPU offloading. It'll be slow but it'll work even with SSD offloading.

Nope, GLM 5.2 is only the latest and greatest in a long line of open-weights models. There are even fully open source models that are comparable to o1-mini (OLMo), or almost-fully-open ones that are comparable to o3 (Nemotron).

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).

2 reasons.

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.

Keyword dynamic, the parameters are quantized on a case by case basis

On a model of this size quantization has much less impact on quality of output. I'm running a 3bit version and find it comparable to sonnet, almost opus.

it is not a flat quant but a dynamic

depends on the color

I run 3bit GLM5.2 and full precision Qwen3.6-27B. GLM is much much closer to frontier models in it's breadth and ability to plan. If you just need to implement Python code from an existing plan Qwen is your choice but it has problem succeeding with more complex tasks that GLM5.2 does not.

As I type this my local GLM5.2 is troubleshooting bugs that Qwen would not be able to handle.

Generally 97.5% token agreement is very positive. Like the article explains, the difference isn’t the model thinking the capital of France isn’t Paris, but rather maybe saying “The capital of France is Paris” instead of “Paris is the capital of France”.

Seems like a good way to shorten the lifespan of an NVME SSD significantly by using up its TB written lifespan, if you let it extensively swap. Also the performance will be absolutely abysmal like 0.1 tok/second.

if sam altman didn't exists this model would most likely not exist as well

You can run it today with that 12gb vram 3060, but I would suggest getting 2 3090s. Use cmoe option. This will keep the attention/route tensors on the GPU and offload the rest to system memory. Try it now and see the performance.

Should work yes.

There are two forms of compression relevant to LLMs:

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.

On ZFS with zstd compression, I am getting 1.34x compressratio for the BF16 weights (across multiple models).

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/

SOTA LLM specific compression achieves around ~54%! https://arxiv.org/abs/2505.06252v3

> ...a bit of an odd question: how well do LLMs losslessly compress, as in for cold storage?

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.

Probably not at all, considering weights are randomly initialized.

Completely worth it. At 6tk a second. If I can get 2 hrs of token generation. That's 2hrs * 3600secs * 6tk = 43200 tokens, at about 10tk to a line of code, that's about 4320 lines. Let's even trim it more and slice it by half. That's 2160 lines of code a day. Most professional programmers can't deliver that much consistently in a day.

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.

Are you talking about Medusa Halo? It's going to support up to 256GB unified memory (up from 128GB for Strix Halo and 192GB for Gorgon Halo). That might just be barely enough to run a 2-bit quant GLM-5.2. It will expand memory bus to 384-bits, vs. 256-bits for Strix Halo which will help with bandwidth (projected to be around 500 GB/sec). But don't expect Madusa Halo-based machines to appear until sometime in 2028.

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.

> I am very excited for local LLMs I think we may have GPT 5.5-xhigh level of performance for under 2000 EUR

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.

At full quantization GLM 5.2 may be close to GPT 5.4. But at Q2 or whatever one needs in order to run it on a pro-sumer device it will be worse.

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.

Even with upcoming AI Max+ PRO 495 we are capped with 192GB, so no...

"GLM 5.2 is just shy of GPT 5.4"... If your running the full model. As in have 750 (FP8) to 1.5TB(FP16) of memory available.

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.

The AMD 395 supports up to 128GB unified RAM. So still not enough even at 1-bit quant unfortunately.