Local LLM Code Completions Are Slow. Here's Why and How to Fix It.

If you've been paying attention to the open-source LLM space lately, you've probably noticed something: models like Kimi K2.5 are getting absurdly good at code generation. Good enough that even commercial tools are quietly acknowledging them as top-tier. And that means running a capable coding model locally is no longer a pipe dream — it's a real option. But here's the problem. You download a model, hook it up to your editor, and... it's painfully slow. Completions take 3-5 seconds. Your fan sounds like a jet engine. You give up and go back to a hosted API. I've been there. Multiple times. After spending way too many hours benchmarking and tweaking local setups, I finally have a workflow that's genuinely usable. Here's how to get there. The Root Cause: It's Not (Just) Your Hardware The first instinct is to blame your GPU. And sure, VRAM matters. But the real bottleneck for most people is a combination of three things: Wrong quantization level — running a full FP16 model when a Q5_K_M w