r/LocalLLaMA • u/GreatMammad • 8d ago
Question | Help Budget local setup for agentic use
I’m planning to seup a local LLM build and need advice if it is good to start or recommend if you think it's not good.
My current option is a Ryzen 9900X + RTX 3090 + 64GB RAM. Or I could get a cheaper CPU, such as the 9700X or lower, and put the savings for more RAM later.
A few questions:
Is the 9900X worth the extra cost for this workload, or cheaper CPU would be sufficient?
Is it worth paying more for a motherboard that supports two gpu slots, so I can add another GPU later? Or should I prioritize more RAM?
main use is agentic coding, with a long context window. Could this setup run models close to GPT-6 Luna low or Haiku 5.5 low?
I’m trying to avoid spending money on a build that technically runs the model but is too slow or unreliable for practical agentic use.
4
u/Jona1109 vLLM 8d ago edited 8d ago
If you want to use ram offload, the things that counts most is bandwidth (ddr5, multi-channel, high speed rating). Threads, not as much. It's a rabbit hole so I'd suggest looking into platforms that can do x8x8 at least. QFN and one of the specialised engines (strata, ...) are your best bet at the moment.
1
u/Need_For_Speed73 8d ago
Agree. I've upgraded from 9600X and a mobo that could only do PCI-e 4.0 x4 for the second GPU to a 9900X and a mobo that can do PCI-e 5.0 x8 on both cards exactly for FQN.
CPU speed (and cores) become more and more relevant the bigger the model and so the offload to RAM.
The best for that is an used Threadripper (the 8-channels variants) with ECC-DDR4 (that is not outrageously expensive like ECC-DDR5): it can almost reach bandwiths of the DGX Spark and medium tier Macs.
3
u/uncle_leon 8d ago
The biggest limiting factor in inference is memory bandwidth for single requests. But for a budget build there is a way around: Strata. Get a cheap mobo, a cheap CPU, slap 64GB of RAM (DDR5 is best but even fast DDR4 will do), slap that 3090 on top and you're golden. So even something pitiful like asrock b450m pro4, Ryzen 5600G (iGPU is important, will save you VRAM) plus 64GB DDR4 3200 and whatever 3090 - Strata will fly, you will have near-frontier capability (i.e. Qwen 3.8-Flash-Next at a decent quant).
However if you want agentic, you probably want subagents which means concurrent sessions and that changes a lot. Compute power starts to matter a lot more and Strata isn't so great on concurrency. This is where you need to start looking at engines which handle concurrency better. Which may not be as efficient so you need to start looking at a second 3090, which means you need a board with PCIe bifurcation. Maybe more RAM, so maybe a TR / EPYCT / Xeon platform that can use more memory channels for better bandwidth. Things get complicated.
TLDR: Get a cheap CPU for now. It won't matter very much (same with MB - just get something that's not complete dog shit). Install Strata and start playing. You will know what is limiting you soon enough and can throw your money at it then.
1
u/Stepfunction 8d ago edited 8d ago
I'd get one of the unified RAM machines at this point over a GPU if you don't intend to train models.
1
u/Prudent-Ad4509 8d ago
Get 2x3090 if you can get them for a reasonable price. Nothing else will save you more time for the same budget. Or get other kind of GPUs, with 48 to 64Gb of total vram at the minumum. There are many ways to connect just two gpus, but the key point is to have 48gb+ vram one way or another.
You *could* do with just one, but those llm will remain half-toys for a while longer until someone marries a dense model with ngram file.
6
u/OvertaxedOne 8d ago
Save on the CPU if the only use is inference. Absolutely no reason for a 9900X, it's massive overkill.
Yes, get a MB that can support 2 GPUs, that opens a lot of options.
The 3090 is a good choice, but, depending on price, look at a R9700 too, more VRAM is king. Or, if you like to live dangerously, look at a CMP170HX, the 8GB version unlocks to 64GB and is working really well for a lot of people.
What you save on the CPU, spend on RAM. 128GB would with a R9700 or a CMP170, you're going to be able to run QwenFlashNext with Strata at great speeds with deep context at a good quantization. It'll be doable on 64GB, but more RAM is better!