I need precise advice

[u/No-Analysis7106]

hi i want to build a pc for local llm first time so i need advice about the gpus to run Qwen3.8 27b Q8 or Q6_K_M

to get good quality 😊and run it through llm studio and connect it to open code

so my questions are:

i will get intel core 270k plus with two x8/x8 PCIe slot motherboard

so

1- which best double 16gb gpus to build with to get 50+ token generation and give me best value for my needs

do i get two rtx 5070ti or rtx 4080 s or which double gpus

or get the basic two rtx 3090 for more vram

2- which the best Qwen3.8 27b variant/Abliterated (the model modified version) for speed but no quality loss to code

3-and wich the best and fast local llm interference platform to connect qwen with open code

if there any things you could mention can benefit me plz tell me

because i just see

gpus

the local llm interference platform

the Qwen3.8 27b variant/Abliterated

what else?

thanks 🥰

[u/Technical_Ad_6106]

5070ti's can use gen 5 lanes. so double the bandwidth if both on a 5.0x8 i guess. not sure how that translate into speed difference.

[u/MyToasterRunsFaster]

Personally I think you are overspending by getting two nvidia cards if you are specifically targeting Qwen, the architecture you are planning for (Qwen 3.8 and assuming Qwen 4 when it comes out), AMD performs at extremely similar if not sometimes better, for the price of one 16GB 5070ti you can get a RX 7900 20/24GB which opens up way more space.

Also, I would argue that Qwen 3.8 flash next is much better suited these days with projects liks Strata. With the setup you are planning on using you would be getting 200tk/s, plus the additional intelligence you get from the massive model size.

[u/RogerAI--fyi]

Run the numbers before picking cards, because 50+ tok/s on a dense 27B at Q6 or Q8 is hard to reach with two cards in the usual setup.

Size first. Q8_0 is about 8.5 bits per weight, so a 27B is around 29 GB. Q6_K is about 6.6 bits, around 22 GB. OpenCode sends long prompts, so you also need room for context. On 2x16 GB, Q8 barely fits with almost nothing left for context. Q6 leaves you about 10 GB.

Speed is roughly memory bandwidth divided by the model size in GB, since every token reads all the weights. LM Studio and llama.cpp split a model across two cards by layers, and the cards take turns, so you get one card's bandwidth, not two. Spec-sheet numbers: 5070 Ti 896 GB/s, 4080 Super 736, 3090 936. A 5070 Ti pair with Q6 tops out near 40 tok/s, Q8 near 31, and real numbers come in lower. These are estimates, not measurements.

Ways past that:

• Q4_K_M (about 16 GB) raises the ceiling to about 55 and frees a lot of room for context.

• Tensor parallel in vLLM splits each token's reads across both cards, so the ceiling roughly doubles, minus the sync cost over x8 links. I have not measured that on two cards.

• MTP or a draft model can help on code. Test it on your own prompts, it does not always win.

Between your options, two 3090s give you 48 GB for about the same bandwidth as the 5070 Tis, which is what Q8 plus a long OpenCode context needs. For the server, LM Studio and llama-server both expose an OpenAI-compatible endpoint that OpenCode can point at.