# Optimization of Armv9 architecture general large language model inference performance based on Llama.cpp

arXiv: https://arxiv.org/abs/2406.10816 · HTML: https://mlsanity.com/paper/2406.10816/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 8 references that are in the map.
- First posted on arXiv 16 Jun 2024.

## Builds on (most cited first, 2 of 2)

- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [PIQA: Reasoning about Physical Commonsense in Natural Language](https://mlsanity.com/paper/1911.11641/) (2019)

## Abstract (arXiv)

This article optimizes the inference performance of the Qwen-1.8B model by performing Int8 quantization, vectorizing some operators in llama.cpp, and modifying the compilation script to improve the compiler optimization level. On the Yitian 710 experimental platform, the prefill performance is increased by 1.6 times, the decoding performance is increased by 24 times, the memory usage is reduced to 1/5 of the original, and the accuracy loss is almost negligible.