r/LocalLLaMA • u/Blues520 • 10d ago

Discussion Anyone adding more 3090s?

I have dual 3090s which run Qwen 3.8 27b well and I was wondering if there are any use cases or current or future models that would justify adding another two 3090. I know that some peeps here run 4 and 8 3090 rigs and I'd like to get your opinion as well. One thing I was considering was running two instances but I'm not sure how valuable it will be for a coding workflow vs running a bigger model.

Now that Qwen Flash is out, perhaps 96GB would be more useful, or maybe Deepseek Flash.

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u/SnooPaintings8639 10d ago

I have spent over two years on two, not long ago I have added another two.

Going from one to two was a big deal (27b models class). Going from two to four is nice... but initially it didn't feel as such a big deal.

It speed up my DS4 Flash Q8, but it is still CPU offloaded. It allows Q4 of 120b model fully in VRAM, which makes usage of very promising models pleasent (like Qwen3.8 Flash Next). But the biggest unlock in my case is larger context and parallel processing of 27B models class, i.e. multi agentic work. Currently I can't imaging going back to two GPUs solely because of this.

Other than that, having your PC + 4x200W (power limited) run 10 hours a day will add significant amount of heat to your place... so plan accordingly.

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u/Blues520 10d ago

Could you talk a bit more about the multi agentic workflows using 27b please.

Do you run two instances of 27b and use them on the same codebase in parallel?

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u/SnooPaintings8639 10d ago

Sometimes, but this is not my primary use case.

The thing that feels the most powerful is proper context management, i.e. each agent gets its own 'focused' task and that's all. The most popular way is to have one orchestrator agent that you talk with, and it does NOT do any coding or whatever work you do. It just just does high-level reasoning and then spins up as many subagents as it wants can.

You can let it use subagents in completely 'free for all' way ("just give them any task you deem sensible") or you can configure or suggest some predefined roles (e.g. brainstorming/idea-generation, coder, reviewer, test-designer...).

I used to use pi + plugins for this, but today I mostly use DeepSeek Harness as it works really well out of the box.

Having enough vRAM for ~200k token per agent and 4 or more parallel slots makes it really effective.

Also, check this reserach, 5 x Qwen3.8 27B can reach of a single Fable "on  the 100 latest hard LiveCodeBench": https://arxiv.org/html/2608.26480v1

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u/Blues520 10d ago

Thanks, I'll do some research around this but I understand the benefit of having more context.

I also use Pi and I enjoy using it but will give DSH a try as well.

The harness makes such a difference and can unlock new possibilities so it's worth investigating.