Tip: TurboQuant Enables Qwen3.5-27B on a 16GB RTX 5060 Ti — Near-Q4_0 Quality at 10% Smaller

r/LocalLLaMA / llama.cpp·medium signal

A practitioner reports fitting Qwen3.5-27B on a single 16GB RTX 5060 Ti using TurboQuant's extreme KV cache quantization — near-Q4_0 quality at approximately 10% smaller model size. Previously this model required 24GB+ VRAM for usable context lengths. Combined with the attn-rot PR nearing merge in llama.cpp, consumer GPU owners can now run 27B-class models for local coding assistance that previously required cloud inference. The key: TurboQuant's Hadamard rotation spreads outlier energy before quantization, preserving quality at extreme bit widths.