Strata-Quantized Qwen 3.8 Flash Next Enables High-Speed Local LLM Inference
A user on r/LocalLLaMA shared benchmarks for a Strata Q4_K_XL quantized version of the Qwen 3.8 Flash Next model. Running on dual RTX 3090 GPUs, the setup achieved inference speeds of 80 to 110 tokens per second at 2,500 prompt processing tokens. Efficient quantization formats allow hobbyists using consumer GPUs to run large language models at significantly higher speeds without sacrificing capability. This enables local LLM users to replace older 27B parameter models with faster, more intelligent alternatives. The user reported that the model matches the speed of Qwen 3.5-35B-A3B while delivering better intelligence than Qwen 3.8 27B. The hardware configuration utilized dual Nvidia RTX 3090 GPUs (48 GB VRAM total) alongside 96 GB of DDR5 system RAM.
## BACKGROUND
Quantization is a model compression technique in machine learning that reduces weight precision (such as converting 16-bit floating-point numbers to 4-bit integers) to lower memory usage and speed up inference. Modern 4-bit quantization variants like Q4_K allow large language models to fit into consumer GPU VRAM while preserving high output quality.