Comment by walrus01
2 days ago
For 10k you can buy a used dual socket Intel or amd based rackmount server with a terabyte of ram, and run models on cpu only at a reasonable speed. Same server would have been 4-5k a couple years ago before ram price rise.
Or buy one on eBay with 512GB that has half its slots populated and then buy the matching 512GB kit to add.
Which CPU gen are you suggesting, is there any writeup on such setup where <10K (not incl. power bill) cpu only rig is giving usable token speeds on latest SoTA open weights models?
In my experience with rig half that cost, entire exercise of running coding models locally has been a huge disappointment.
Cost/Value when compared to cloud services is just not there, but I see the merit for those who value privacy over quality of output and want a backup of huge condensed corpus of data within their control.
Kudos to OP though, They had clear goals and they achieved it.
I realized I didn't answer the CPU question, as a very quickly chosen example from eBay, there's a Dell R740XD with two Xeon Gold 6254 CPUs, 768GB RAM for sale for something like $5799 USD right now. I'm sure if I put some more time into it I could piece together something with a full terabyte for around the same price. Or faster/better CPUs, more core count CPUs by buying the system with no RAM, or minimal RAM (64GB) and then adding the DIMM kits from the more reputable refurb server part vendors on ebay.
It won't be fast at all, for certain, but it'll have enough memory to prove a configuration and be able to really use gargantuan GGUF format LLMs in the latest compiled llama-server. Re: electricity, I pay the equivalent of $0.07 ro $0.09 USD per kWh so it's not an extreme burden to have a theoretical 500W server running. Something like $35 to $50 of electricity a month if it's 500W 24x7.
Xeon Scalable in general seems like a good idea due to 6-channel (relatively) inexpensive RDIMM memory, but I've been reading that NUMA kills inference performance. Anyone got experience with multi-socket systems? IIRC even within the socket these cpus are divided into sub-numa nodes.
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Would be nice if you could somehow connect GPU-levels of parallel floating point cores to that amount of memory. I guess that's what the big AI datacenters are doing, but how can we do that on a budget?
I think there is a good sized population of people who absolutely don't want to submit everything they do to an off site service, or let their content be used for unknown training purposes, and will tolerate slowness at 1 to 10 tok/s as a tradeoff.
Or people who want or need to run an uncensored (abliterated) gguf file to deal with controversial topics that a paid LLM service will refuse to work with or ban you for.
Not just controversial but also regulated areas. Virtually every law firm would be interested on locally-hosted AI at a reasonable price. So too ever medical research lab. Every CGI firm doing work for film/TV. And all the video game developers.
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I would suspect that one would buy based on mem-bus & PCIe bus speeds more than CPU for this, and just dial down the CPU parameters to save power. Most of the time and power will be consumed by memory and bus transfers because the CPU will mostly be waiting to the right set of weights and factors to multiply.
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This was the case a few years ago, but now the RAM costs twice that much by itself, and the server is sold without RAM.