I compressed GPT-2 to run on an Arduino! Here's how I did it with KVQuant. The Problem: LLMs need huge memory for key-value caches during inference. The Solution: 4-bit KV cache quantization that reduces memory 4x with <1% accuracy loss. Results: GPT-2: 512MB → 128MB (4x reduction) LLaMA-7B: 8GB → 2GB LLaMA-70B: 280GB → 70GB Code: github.com/AmSach/kvquant