Quantized Local LLMs: 4-bit vs 8-bit Performance Analysis

Local language model deployment has become practical through quantization, which compresses model weights from 16-bit to 4-bit or 8-bit formats, enabling 8-billion parameter models to run on consumer GPUs instead of requiring enterprise hardware. Testing reveals 8-bit quantization maintains near-identical quality to full precision models with less than 1% degradation, while 4-bit quantization shows 2-3% quality loss but delivers 35-72% faster inference speeds and fits within 8GB VRAM constraints that make deployment accessible to mainstream users.