I play with local LLMs a lot. I've spent more on hardware than I should. I'm friends with a local group of people who have spent a lot more than I have.
The warning I would have for everyone is to temper your expectations and read the fine print carefully. The big build in article starts off with a $40K budget and then includes 4 GPUs that are $12K each. For those doing the math, this build is going to cost more like 50-55K.
Local setups also often rely on quantization and techniques like REAP to fit the models on their hardware. You will read a lot of claims that 4-bit quantization is lossless, but those claims come from KL divergence measurements on a small corpus. Use one of these 4-bit models on long context coding tasks and the quality will be noticeably less. Even for non-coding tasks like dataset analysis, I can measure a substantial quality difference between 4-bit models, 8-bit quants, and even some times the full 16-bit source.
This article is also encouraging the use of a REAP model, which means someone has cut out some of the weights to make it smaller. The idea is to remove weights that are less useful for certain tasks, but again this is going to reduce the overall quality of the output.
The trap is that people say "I'm running GLM-5.2 locally!" and it sounds amazing when you look at the GLM-5.2 benchmarks. However they're not actually running GLM-5.2, they're running a model derived from GLM-5.2 that discards most of the bits and drops some of the experts. It does not perform the same as what you see in the benchmarks. In my experience, the divergence between a quantized/REAP model and the parent model is unnoticeable when you try it on very small tasks or chat, but becomes painful when you start trying to use it on long-horizon tasks where little errors start compounding.
Then you get into the slippery slope of thinking you're $50K deep into this project, but what you really need is just one or two more of those $12K GPUs to use the next level of quantization that might improve the quality a little more and make your investment worthwhile...
Well you could make a REAP with better input prompts on longer context then. It’ll improve the REAP quality
CamperBob2
today at 4:48 PM
All very true. Right now, running GLM 5.2 at its full BF16 quantization level needs 1.5 TB of VRAM. You can't run this locally at a usable speed for less than $250K or so, and frankly I'd be surprised if it could be done for less than $500K.
The best NV4FP quant for 5.2 appears to be lukealonso's at https://huggingface.co/lukealonso/GLM-5.2-NVFP4, and it is capable of good throughput (75-100 tps) without losing much reasoning performance. Allowing for overhead for the KV cache and other requirements, this quant will (barely) run in 8-way tensor-parallel mode on 8x RTX 6000 cards. Not too long ago it was possible to put an 8x machine together for less than $100K USD, but that's probably not true now, assuming you buy all-new components.
It'll almost certainly be worth it, given the abusive behavior we've seen and will continue to see from the major closed-model providers. If I hadn't already put a similar rig together, I'd be kicking myself. But getting it running well is by no means as simple as buying a bunch of RTX6K cards and calling it a day, and people need to know what they're getting into.
Local AI is in its Altair and IMSAI days. There's no turnkey Apple II or C64 on the market yet, much less an IBM PC. Hardware, yes -- you can buy a capable box off the shelf from various vendors -- but you have to be prepared to take up a whole new hobby when it comes to getting a complete system working well.
> It'll almost certainly be worth it, given the abusive behavior we've seen and will continue to see from the major closed-model providers.
The proper financial comparison for GLM-5.2 would be one of the providers on OpenRouter or renting a server as needed. Compare apples to apples.
You will almost certainly never break even compared to paying per token.
Local LLMs at this scale are only worth it if you have extremely strict requirements that data not leave the premises.
Or if you want to hedge against the various tail risks of third-party providers raising prices or denying you service or somehow abusing your data...
> hedge against the various tail risks of third-party providers raising prices
They could 10X the prices and you’d still be better off. It’s also unlikely that prices go up enough to warrant a $100K local investment to prevent paying a couple bucks per million tokens.
> or denying you service
I guess you’re not familiar with OpenRouter? There are many providers there. There are providers outside of OpenRouter. There will always be someone to take your business.
> or somehow abusing your data...
If data security is your concern then you’re better renting a server as needed still.
If you cannot tolerate any data leaving, then local models are the only way. You pay a high premium for it!
incrudible
today at 5:10 PM
Raising prices is not a tail risk, anything a local LLM setup can do for you can be done by any cloud provider, with the same capex as yours (or less), there is no moat here, so it is highy price competitive and will remain so. If you want to speculate on hardware shortages, that is a different business altogether and you need no janky garage setup to profit.
CamperBob2
today at 4:56 PM
Also agreed, it's definitely a sucker's game to run a high-end model locally, by any objective measure.
Still... if it's not your weights, running on your box, you're always going to be behind somebody else's 8-ball. Everybody has to decide for themselves where their priorities lie.