Optimizing your LLM in production

it's all about shaving off milliseconds and saving cash. optimizing an LLM isn't some magic bullet; it's brutal trade-offs between quality and compute. you're either throwing massive GPU resources at inference or you're accepting garbage quality.

quantization schemes like QLoRA or 4-bit loading are the entry point, obviously. but fine-tuning requires way more commitment. if you want serious performance gains, you're digging into custom kernels or distillation, which is pure R&D, not standard engineering.

honestly? most teams just pick the easiest quantization method and pray it works. it's a false economy. you gotta measure the actual latency and the downstream cost of those approximations before you deploy.