This comprehensive study evaluates quantization formats (FP8, INT8, INT4) across the Llama-3.1 family, revealing optimal efficiency-accuracy trade-offs for LLM deployment.

• FP8 quantization performs virtually losslessly across all models and tasks
• INT8 formats show minimal accuracy loss while delivering substantial performance gains
• INT4 quantization offers dramatic acceleration but with meaningful accuracy degradation
• Model scale matters: larger models (70B+) better preserve accuracy under aggressive quantization

For engineering teams, these findings enable evidence-based decisions when selecting quantization strategies to balance inference speed, memory requirements, and output quality in production environments.

"Give Me BF16 or Give Me Death"? Accuracy-Performance Trade-Offs in LLM Quantization