: Recent research (April 2026) has further optimized this by creating Fast NF4 Dequantization Kernels that achieve 2.0–2.2× speedups on NVIDIA GPUs. ⚠️ Alternative Interpretation
In the context of computer science and machine learning, refers to 4-bit NormalFloat , a specialized quantization data type introduced in the seminal paper QLoRA: Efficient Finetuning of Quantized LLMs by Tim Dettmers et al. (2023). 📄 Core Concept: The QLoRA Paper NF4.rar
: An information-theoretically optimal data type for normally distributed weights. It uses 16 quantization levels based on the quantiles of a standard normal distribution. : Recent research (April 2026) has further optimized
: Compresses 16-bit weights to 4 bits, effectively reducing VRAM usage by ~75% (e.g., a 65B parameter model can be loaded with ~35GB instead of ~130GB). 📄 Core Concept: The QLoRA Paper : An
The paper explains why NF4 is superior to standard 4-bit integers (Int4) or floating-point (Float4) formats: