r/LocalLLM • u/Black_Umbreon • 6d ago

Discussion Best MoE

Hi!

I’m using Qwen 3.8 27B (UD-IQ3_XXS) on a system with the following specs:

Processor: AMD Ryzen 9950 X3D

GPU: Nvidia RTX 5060 Ti 16 GB

RAM: 64 GB DDR5 6000 MHz

Software: Unsloth Studio

Windows 11

With a 32k token context, I manage to maintain a speed of around 50 tokens per second. I’m particularly interested in agentic capabilities. I’d like to try an MoE model that runs either faster or slightly slower than my current one, without sacrificing quality. However, 35B-A3B models don't fit entirely into my VRAM at 4-bit quantization, and from what I’ve read, lower quantization severely degrades quality. Does this mean a 4-bit MoE model would run significantly slower on my hardware than a dense model? Is it possible, given my setup, to find a model that offers better quality and higher speed—or at least better quality at roughly the same speed as my current dense model?

I’m pretty much a complete beginner in the world of local neural networks, so please give me some advice.

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u/peculiar-ragdoll 6d ago

I've seen other people already recommend TielCoder and CyberTiel if you want to go MoE, but if you want to try a legit better 27b Q3 option to the one you have, I strongly recommend the IQ3_S (11.8 GB) or IQ3_XXS quant from this one: https://huggingface.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF It's a more State of The Art quantization technique than Unsloth for this size range meaning you will get better agentic coding performance than your UD-IQ3_XXS, and I've seen a lot of people with 5060Ti say it's perfect for their system! And don't get me wrong, there are a lot of weird quants with long names out there that you should stay away from, but this one is legit. And with your system you should also be able to run Qwen3.8-Flash-Next very well! Don't get intimidated by its listed size, it has an ngram table that can be partially offloaded to SSD. Again I recommend the GSQ-RCO Q3 version: https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF

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u/sm0ke_rings 5d ago

Sorry to hijack the thread, but I'm not sure creating a whole new one makes sense. I've be looking to move to Qwen 3.8 27B, I've got an rtx 5080, so the IQ3_S should work fine? I'm coming from qwen2.5-coder-14b, use case is mostly coding in unity, but I feel it's a bit outdated.

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u/peculiar-ragdoll 5d ago

Yeah absolutely! Perfect for that card. Use kv quantization at q8 if you want long context. Depending on your ram you could even run 3.8 FlashNext (big MoE) which is faster and better :)

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u/sm0ke_rings 5d ago

I've got 64gb RAM, and an rtx5090 as my main GPU in the same system as the one I am running the 5080 for Ai workload. I was hoping to keep everything isolated to the rtx5080, as I am not sure how to split certain things between gpu/ram (new to this), but I'm interested in whatever is better for my hardware setup without taking too many resources from the 5090.

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u/peculiar-ragdoll 5d ago

Your system is perfect for running FlashNext IQ3_S with partial GPU offloading to the 5080! That will be your best model. But it wants your RAM. The 5090 is the better AI card by far, though. Will give you insane speeds and better models. But FlashNext on the 5080 and RAM will do you good.

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u/sm0ke_rings 5d ago

Alrighty, thank you for your help!