This series is for quantization and low-precision serving. It deserves its own track because quantization touches representation, error control, calibration data, kernel support, KV cache, memory bandwidth, and quality regression.

Existing Posts

1. Numeric Types in Neural Networks: FP32, BF16, FP8, INT8, and INT4
2. A Survey of LLM Quantization: From Linear Quantization to Codebooks

Planned Posts

• KV cache quantization: beyond weight memory, the cache is often the real footprint
• FP8 serving: E4M3 / E5M2, activation scales, and Tensor Core paths
• INT4 weight-only serving: why saving memory does not always mean going faster
• GPTQ / AWQ / SmoothQuant engineering boundaries
• NF4 / AQLM: why lower bit widths need codebooks
• Quantization benchmarks: measuring the quality, speed, and memory triangle

Questions Each Post Should Answer

• Are we quantizing weights, activations, KV cache, or a communication/storage format?
• Does the benefit come from capacity, HBM bandwidth, Tensor Core throughput, or disk size?
• Does the error mainly come from outliers, scale granularity, rounding, or clipping?
• How do vLLM / SGLang load, observe, and roll back this choice?