# SIMD Aligned Memory Vectorization and Super-block K-Quants > Source-anchored research for SIMD Aligned Memory Vectorization and Super-block K-Quants. Run status: COMPLETED. Parent: [Local Inference Micro-Architectures: Signaling, Quantization Superblocks, and Kernel Pipeline Scheduling](https://llms-explorer.com/tree/inference-microarchitectures-and-kernel-pipelines/) · 2 facets · 2 facts · page: https://llms-explorer.com/tree/simd-aligned-memory-vectorization-and-super-block-k-quants/ ## Structure and components - Super-block K-quants (Q4_K) group 256 weights into chunks with a shared FP16 super-block scale and eight 6-bit sub-block scales, reducing quantization noise in attention projection layers. (high confidence) — [source](https://github.com/ggerganov/llama.cpp/blob/master/docs/gguf.md) ## Facts and statements - 32-byte memory alignment prevents unaligned memory access penalties and cacheline splits during vectorized 256-bit SIMD load instructions across host CPU and GPU threads. (high confidence) — [source](https://github.com/ggerganov/llama.cpp/blob/master/docs/gguf.md)