I've got Strata running the IQ3_S quant of Qwen 3.8 Flash Next on Strix Halo machines, with 256k KV cache (context length), at a maximum of over 40 tokens per second for short prompts, in Windows 11. It seems to average around ~35 tokens per second on very long prompts.
That IQ3_S version of the model is what Strata has documented as achieving the same benchmark scores as the full precision version.
Strata is not optimized for Strix Halo, so this is a very happy result - faster than all the other versions of the QFN model I've run in LM Studio and other engines in Windows, and the quality is definitely very good.
Clearly, Strata and the QFN 3.8 model are built to take advantage of the CUDA stack. Even the old RTX 3080ti mobile GPU, with only 16Gb VRAM and 64Gb RAM performs faster than the Strix Halo. It's not really apples to apples, though, because the Strix Halo above was configured to enable 4x the context length at IQ3_S (highest quality). That's where all that Strix Halo VRAM comes in handy.
I love that users don't need to wipe Windows and install Linux on Strix Halo to get very good performance from Strata - though the fastest solutions on Strix Halo are still possible only with Linux installed.
Still, the IQ2_XS quant that runs 256k context on the mobile 3080ti in Windows with 64Gb RAM is quite capable & knowledgeable, and you can buy the hardware to run it for about 1/3 the cost of any Stix Halo machine - it's actually affordable, even for casual hobbyists (~$1000 range, where the next step up to clustered DGX Spark machines is now $14,000-$20,000). Add a few hundred dollars of RAM, and you can run IQ3_S at 256k on machines with inexpensive RTX GPUs. So there are options and alternatives to support your budget and preferences.
I'm becoming a huge fan of Strata :)