Quantization Handbook Demystifies Low-Bit Inference

This is a valuable antidote to treating “4-bit” as a complete performance specification: quantization quality and speed depend on calibration, outliers, granularity, kernels, and hardware. Its fundamentals-first structure makes a complex deployment topic approachable for developers.

• –Connects basic scale-and-zero-point math to modern LLM quantization methods
• –Clarifies why per-channel and per-group schemes can preserve quality better than coarse per-tensor scaling
• –Covers both weight-only and activation quantization, giving readers useful deployment context
• –Emphasizes that lower precision reduces memory, but faster inference still depends on backend support and hardware
• –Useful reference for choosing between PTQ, QAT, GPTQ, AWQ, and related approaches