On many models that I tested in past context quantization had very bad effect on model performance. However qwen3.8 27b is different.
I'm now running NVFP4 quantized both weight and cache on my RTX5090 and getting excellent results: 264k cache allocated for pool, 10k tok/s prompt processing, 200 tok/s generation for single stream, or 801 tok/s generation for 8 concurrent streams.
Also have about 2Gb vram left for use of OS.
my coding agents regularly reach 200k context used without noticeable degradation.
I'm now running NVFP4 quantized both weight and cache on my RTX5090 and getting excellent results: 264k cache allocated for pool, 10k tok/s prompt processing, 200 tok/s generation for single stream, or 801 tok/s generation for 8 concurrent streams. Also have about 2Gb vram left for use of OS.
my coding agents regularly reach 200k context used without noticeable degradation.
P.S. I used setup from: https://github.com/seanyourhighness/vllm-sm12x-nvfp4-dflash2