Precision Tuning for llama.cpp: Squeezing Every Drop of GPU Performance

Optimizing the --n-gpu-layers parameter is crucial for local LLM users to balance speed and memory constraints on consumer hardware. This guide provides a strategic approach to VRAM management, accounting for both model weights and KV cache growth, which directly impacts inference efficiency. Mastering this tuning process allows developers to run larger models faster without hitting memory bottlenecks.