A detailed comparison of 4-bit vs 8-bit quantization for running LLMs locally, covering formats like GGUF (Q4_K_M, Q8_0), AWQ, GPTQ, and EXL2. Benchmarks on Llama 3.1 8B show 8-bit formats stay within 0.5% of FP16 quality while 4-bit formats drop 1.8–2.9% depending on format. 4-bit models run 35–72% faster and use roughly half the VRAM, enabling 7B/8B models to fit on 8GB GPUs. AWQ and Q4_K_M consistently outperform GPTQ-4bit. Practical guidance covers format selection by hardware, use case, and common pitfalls like confusing Q4_0 with Q4_K_M.
Table of contents
4-bit vs 8-bit Quantization ComparisonTable of ContentsWhat Is LLM Quantization and Why Does It Matter for Local Inference?How 4-bit and 8-bit Quantization Actually WorkBenchmark Methodology4-bit vs 8-bit: Quality Benchmark ResultsSpeed and Resource Usage ComparisonChoosing the Right Quantization for Your Use CasePractical Tips for Running Quantized Models LocallySummary and Reproducibility NotesSort: