: A feature to handle memory spikes during training by offloading to CPU RAM. 🔬 Key Technical Details
: To reduce the memory footprint of LLMs (like Llama) enough to fit on a single GPU (e.g., a 24GB RTX 3090) while maintaining full 16-bit performance. NF4.rar
: A process that quantizes the quantization constants themselves to save additional memory. : A feature to handle memory spikes during
: 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 : Recent research (April 2026) has further optimized
The term "NF4" is central to this "long paper" which revolutionized how large language models (LLMs) are fine-tuned on consumer hardware.
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