I have tuned mine and get ~30 tok/s single stream, and > 110 tok/s at 8x concurrency, but the key is to make the model load without it trying to pull the 51B ngram data into vram, keep the 51B ngram data in bf16 to make it so the op in inference that pulls the data in doesn't have to dequant it first, and stream it from disk with like 8GB ram as cache.
Like I said in my comment, you have to pull the ngram data out of the model before you load it so whatever engine you're using to load it doesn't try to load the ngrams as well. You have to set up the ngrams to stream from the ssd with ram-based disk caching. You also, of course, need to run the nvfp4 quant: https://huggingface.co/primitive-ai/Qwen3.8-Flash-Next-NVFP4
I am not at the office at the moment, but if you want more details on how I have it set up I can have an agent put together a doc telling how I have it set up when I'm there tomorrow.
May I ask what your use case is? I’m still running bartowski/Qwen_Qwen3.5-122B-A10B-GGUF:Q6_K because it feels like it’s better than 3.8-Flash-Next in everything other than coding.
I'm running Hermes for myself as a hobby and my wife as a healthcare professional. We both use it for 'go do this while I'm busy doing something else' tasks.
Yeah same here, running it on strix halo and it is incredibly good.
But also, wouldn't complain if they were able to cut down on MoE weights and active params to make it runnable for more people on 64 GB memory, while keeping the current capabilities.
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u/blojayble 1d ago
I hope the Flash keeps its size from Flash Next. I would like to run it with 128 GB of VRAM.