A while ago I posted 15 tok/s output and 100-120 tok/s prompt processing with the IQ3_XXS quant on a 12GB RTX 5070 using llama.cpp. Since then I built my own inference engine for this one model and this kind of PC. The same IQ3_XXS now runs at ~65 tok/s output and ~430 tok/s prompt processing, and the 2-bit quants run faster still using RCO-GSQ quantization. Using: 64GB DDR5 (5600) 12GB RTX 5070 SFF (Gigabyte) Ryzen 5 7600 CPU Windows Output (tokens/s) on 128K context: Q2_0 (equivalent to unsloth Q3): 65.1 IQ2_XS (equivalent to unsloth Q4): 52.0 IQ3_XXS (equivalent to unsloth Q5): 44.8 Prompt processing (tokens/s) on 128K: Q2_0: 543 IQ2_XS: 472 IQ3_XXS: 414 Requirements: Q2_0 = 37.6GB minimum in RAM+VRAM IQ2_XS = 39.2GB minimum in RAM+VRAM IQ3_XXS = 47GB minimum in RAM+VRAM Vision encoder = 0.91GB additionally You can now one click install and run the engine with low cost hardware (currently only optimized for CUDA). GitHub: https://github.com/Niko1221/Strata Model: https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF   submitted by   /u/KnownAd4832 [link]   [comments]