Local LLM Quantization Guide: GGUF Q4_K_M vs Q8_0 vs EXL2 Benchmarks

Selecting the right quantization format determines whether a model fits in your VRAM while preserving perplexity scores:

Recommendation Matrix - **Q4_K_M**: The sweet spot for 95% of users. Consumes ~50% less memory with less than 1.5% perplexity degradation. - **Q8_0**: Near-identical to 16-bit FP16 precision. Ideal for strict code generation and mathematical reasoning when VRAM permits. - **EXL2 (ExLlamaV2)**: Maximum throughput on NVIDIA RTX GPUs. 2.5x faster token generation than llama.cpp at 4.0bpw.

Rule of Thumb A 32B model quantized to Q4_K_M (~20GB VRAM) consistently outperforms an unquantized 14B model (28GB VRAM) on real-world benchmarks.