Guide: Running Qwen 27B with 100K Context on 16GB AMD VRAM

A developer shared a custom configuration for running the 27-billion parameter Qwen 3.8 model (Q4_XS quantization) with a 100K context window on a single 16GB AMD RX 7800 XT GPU. Built using llama.cpp with Vulkan support, the setup achieves decoding speeds of approximately 30 tokens per second. Running a 27B parameter LLM with an extensive 100K context window was previously considered impractical on consumer hardware with 16GB VRAM. This guide shows how modern quantization and KV cache optimizations make large-context inference accessible without expensive enterprise-grade GPUs. Memory efficiency is achieved by compiling llama.cpp with Vulkan (`-DGGML_VULKAN=ON`) and employing quantized KV caching (`q8_0` for keys and `q5_1` for values) alongside Flash Attention. Additional flag tuning—such as setting batch size to 2048 and ubatch size to 512—prevents memory overflow while maintaining fast decoding speed.