(I want to spend no more than $10k. And I want to run a model comparable to today’s SOTA.)
Or buy one on eBay with 512GB that has half its slots populated and then buy the matching 512GB kit to add.
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.
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.
So for optimal speed the models must be quantized in this format.
It is very likely that with INT8 models those CPUs are fast enough so that the inference throughput is limited by the memory bandwidth (384-bit interface to DDR4-2933 per socket, i.e. 282 GB/s for both sockets).
The memory throughput for such an old server is very similar to an AMD Ryzen Strix Halo, NVIDIA DGX Spark or Apple M5 Pro, but it has much more memory.
The inference speed should be very similar to those, but with bigger LLMs.
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.
The largest high performance compute ec2 offering, the c9g.metal-48xl , maxes out at 384GB RAM and already costs a shitload.
The m9gd.48xlarge and m9gd.metal-48xl both have 768GB RAM and I cringe to think what they cost monthly. I just did the math on one of these and it costs $12 per hour, or $289 a day, or $8900+ for one month.
Also plenty of Europeans or people from other locations may consider it as an unacceptable risk factor to put their "off site" self hosted AI stuff with an American controlled company. Particularly if the servers are physically in the USA.
Yes, it's a boatload of cash, but that's a €13,000 GPU and €20,000 of RAM at present prices. There is a segment of businesses where a fixed €28k/year bill is going to be preferred over plonking down €40k for a (theoretically) depreciating asset and ongoing colocation costs.
And yet basically all AWS customers are doing exactly that. Turns out that making CAPEX "someone else's problem" is worth quite a lot to many businesses
can we trust any US based service to guarantee privacy and confidentiality? especially to us european frienemies?
Insert your dedicated hosting provider of choice for 'AWS' (somewhere like Hetzner will be cheaper anyway).
But in general, AWS hosts are yours, running your code, with your security policies enforced. Sure, the US government can silently subpoena the contents thereof, but aside from that fairly extreme case, it's not like AWS is handing your data over to 3rd parties.
The question is, will you want to run a model comparable to today's (meaning 2026) SOTA in 2028? Humans always want the latest shiny LLM model.
- Asking Qwen to review project docs (requirements, user stories, etc) so that "we" can evaluate an iterate on an API design. Then back-and-forth chat about possible design directions. Then I ask for a rough-sketch plan of the one I'm interested in. I provide some tweaks to the plan and request a final plan in full detail. I switch to build mode and say go; everything is written to spec.
- Asking Qwen to write a suite of tests covering X, Y, Z issues with permutations A, B, C per issue.
- Asking Qwen to edit the shape of a CNN to insert auxiliary branches for intermediate supervision, and to extract out part of the network as a modular component with parameterized architecture.
I have less experience with the dense 27B because it's too slow to use on Apple Silicon. But regardless of which model you try, I would recommend trying a full-fat cloud hosted version of it first, so that you can get a sense of what it's capable of when the inference stack is correctly configured. LLMs are very sensitive to quantization formats, discrepancies in chat templates, etc. That kind of stuff is make-or-break.
The thing is, everyone has their own variant of "qwen3.6 27b" depending on the launch parameters, ranging from "SOTA in its class" to "completely broken"
But if we can believe you that it's doing what a Claude model was doing a year ago then I'd say: OMG no I really never want to go back to that level of frustration getting an agent to do what I want it to do.
While it probably won't matter enough to change your mind, remember that you've gotten better at extracting value from all models than you were a year ago - plus the harnesses and other tools have gotten a lot better too.
What could be more valuable than outputting the exact thing you asked for?
Expectations seem to be rising at a faster rate than models can improve.
The hard thing is always keeping complexity low and being ZeroOps.
This is based on the observation that the medium-sized open weight models (~20-35b) are very able to one-shot smaller discrete tasks but seem to lose their way project managing themselves through larger tasks that have multiple steps.
I have a 3 Mac Studio set up and built an IDE / harness (propelcode.app) and would be interested in contributing if you’re open to collaboration
Docs:
With only 1 PCIe 5.0 SSD, the reading throughput is still significantly more than 10 times faster than on author's system.
So it is likely that inference speeds around 1 token/s are achievable on something like a NUC mini-PC.
On a level playing field the expression of virtuosity can outshine those who have never known any instrumental limitations at all :)
When pulling way more than your own weight happens like for few others.
There should be an award for getting the most out of the electronics rather than trying to reach orbit by building the tallest pile of e-waste.
First Prize right before your eyes !
Grande praise !
And just starting to ascend toward an unconquered summit that others find forbidding ;) Or they find uninteresting since the limit naturally lies on firm earth somewhere below the stratosphere.
These days, can "ordinary people" afford 24GB of ram and half a TB of NVME ssd?
sigh
You can, right now, buy a brand new Mini-PC at or above this spec for $600 at retail [1]
Of course, if you want it in a desktop format with a much faster CPU, its going to cost you more.
[1]: https://www.amazon.com/GMKtec-M6-Ultra-Upgraded-Computers/dp...
32G RAM, nvme 1TB, core ultra 258V.
Looking at the prices now... Wow, was I lucky.
Tried some of the 7b models locally, more than usable, around 30token/sec, not with the NPU, but using the ARC integrated GPU.
I am a noob for this, but I guess it's time to experiment more with this local setup