211–220 of 328 posts
I've got access to a 192GB RAM Mac Studio, which is below the stated minimum RAM. Can swapping off fast disk be used to make it work out, especially since it's MoE?
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.
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pretty much any of them, dude, as long as you have enough RAM, since it uses unified RAM and a powerful SoC CPU/GPU. Literally any M-class model, but the M5 is currently top tier.
The DGX Spark has basically the same memory bandwidth as a M5 Pro, and far more than a M5. 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.
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…
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"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 tha…
The cost of local hardware is amortized if a whole team uses it instead of just 1 dev (GPUs are extremely underutilized if you launch just 1 generation stream). I'm not sure why everyone always assumes solo devs with Macs. We've just ordered a large datacenter-grade node for use by the whole dev team, and the calculations show that it's going to cost the same amount of money if we kept using AWS Bedrock (infosec reas…
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).
So close! My machine with 192GB RAM + RTX 3090 24GB can almost run this. It says it needs 24GB of VRAM and 256GB of RAM for MoE offloading. https://unsloth.ai/docs/models/glm-5.2#usage-guide In a prior thread, someone said it would take $500k in hardware: https://news.ycombinator.com/item?id=48629970
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”?
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We do want privacy, and we also want to own the hardware so the US can't just turn it off whenever it feels like it. I think the main reason not to run locally is to get the full models instead of quantized versions.
> We do want privacy, and we also want to own the hardware so the US can't just turn it off whenever it feels like it. 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…
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.
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> GPUs are extremely underutilized if you launch just 1 generation stream 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.
Some of the inference engines can process multiple requests in parallel more efficiently than doing them sequentially. Not sure of the exact mechanism but e.g. llama.cpp's llama-server can do this (you tell it the number of slots to have when starting, then fire HTTP requests at it and it batches them together when it can). 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.
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Run one task, while you do another? Or while you sleep / eat / rave?
While my colleagues are running 6 parallel agents at 50-100t/s each, with an actual SOTA model? Don’t you think I‘d get fired after a few weeks of that?
I run Q4_K_XL. All it takes to run to get about 6tk/sec is 512gb of ram and 2 3090 GPUs with llama.cpp -cmoe. I also have crappy DDR4, 2400mhz, 3200mhz will bring that speed up to about 9tk/sec. I also have ok 32core epyc CPU, a better 64core would bring it up to about 11tk/sec. I did a budget build before the crazy hardware cost and I regret it everyday. Nevertheless, it's fantastic being able to run this model at h…
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).
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.