Qwen3-235B-A22B-FP8

Good! An MoE.

Ideally its maxium context lenght of 131K but i’m willing to compromise.

I can tell you from experience all Qwen models are terrible past 32K. What’s more, going over 32K, you have to run them in a special “mode” (YaRN) that degrades performance under 32K. This is particularly bad in vllm, as it does not support dynamic YaRN scaling.

Also, you lose a lot of quality with FP8/AWQ quantization unless it’s native FP8 (like deepseek). Exllama and ik_llama.cpp quants are much higher quality, and their low batch performance is still quite good. Also, VLLM has no good K/V cache quantization (its FP8 destroys quality), while llama.cpp’s is good, and exllama’s is excellent, making it less than ideal for >16K. Its niche is more highly parallel, low context size serving.

My current setup is already: Xeon w7-3465X 128gb DDR5 2x 4090

Honestly, you should be set now. I can get 16+ t/s with high context Hunyuan 70B (which is 13B active) on a 7800 CPU/3090 GPU system with ik_llama.cpp. That rig (8 channel DDR5, and plenty of it, vs my 2 channels) should at least double that with 235B, with the right quantization, and you could speed it up by throwing in 2 more 4090s. The project is explicitly optimized for your exact rig, basically :)

It is poorly documented through. The general strategy is to keep the “core” of the LLM on the GPUs while offloading the less compute intense experts to RAM, and it takes some tinkering. There’s even a project to try and calculate it automatically:

https://github.com/k-koehler/gguf-tensor-overrider

IK_llama.cpp can also use special GGUFs regular llama.cpp can’t take, for faster inference in less space. I’m not sure if one for 235B is floating around huggingface, I will check.

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Side note: I hope you can see why I asked. The web of engine strengths/quirks is extremely complicated, heh, and the answer could be totally different for different models.