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